Random Forest Models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pkg.clone(\"https://github.com/henrikbostrom/RandomForest.git\")\n",
    "#Pkg.add(\"Convex\")\n",
    "#Pkg.add(\"ECOS\");# Fit random forest regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mInstalling Julia nodeps kernelspec julia-nodeps-0.6\n",
      "\u001b[39m[InstallKernelSpec] Removing existing kernelspec in /Users/AlexLeong/Library/Jupyter/kernels/julia-nodeps-0.6\n",
      "[InstallKernelSpec] Installed kernelspec julia-nodeps-0.6 in /Users/AlexLeong/Library/Jupyter/kernels/julia-nodeps-0.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(`/Users/AlexLeong/.julia/v0.6/Conda/deps/usr/bin/jupyter kernelspec`, \"julia-nodeps-0.6\")"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using IJulia\n",
    "IJulia.installkernel(\"Julia nodeps\", \"--depwarn=no\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using RandomForest\n",
    "using DataFrames\n",
    "using ECOS\n",
    "using PyPlot\n",
    "using Convex\n",
    "set_default_solver(ECOSSolver(verbose=0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "court_test = readtable(\"courtnewf_test.csv\")\n",
    "court_train = readtable(\"courtnewf_train.csv\")\n",
    "court_valid = readtable(\"courtnewf_valid.csv\")\n",
    "\n",
    "office_test = readtable(\"officenewf_test.csv\")\n",
    "office_train = readtable(\"officenewf_train.csv\")\n",
    "office_valid = readtable(\"officenewf_valid.csv\")\n",
    "\n",
    "school1_test = readtable(\"school1newf_test.csv\")\n",
    "school1_train = readtable(\"school1newf_train.csv\")\n",
    "school1_valid = readtable(\"school1newf_valid.csv\")\n",
    "\n",
    "school2_test = readtable(\"school2newf_test.csv\")\n",
    "school2_train = readtable(\"school2newf_train.csv\")\n",
    "school2_valid = readtable(\"school2newf_valid.csv\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# courthouse init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_court_train = court_train[:Courthouse_y]\n",
    "y_court_train = convert(Array, y_court_train)\n",
    "X_court_train = court_train[1:19]\n",
    "X_court_train = convert(Array,X_court_train)\n",
    "reshape(y_court_train, 5227, 1)\n",
    "reshape(X_court_train, 5227, 19)\n",
    "\n",
    "y_court_valid = court_valid[:Courthouse_y]\n",
    "y_court_valid = convert(Array, y_court_valid)\n",
    "X_court_valid = court_valid[1:19]\n",
    "X_court_valid = convert(Array,X_court_valid)\n",
    "reshape(y_court_valid, 1742, 1)\n",
    "reshape(X_court_valid, 1742, 19)\n",
    "\n",
    "y_court_test = court_test[:Courthouse_y]\n",
    "y_court_test = convert(Array, y_court_test)\n",
    "X_court_test = court_test[1:19]\n",
    "X_court_test = convert(Array,X_court_test)\n",
    "reshape(y_court_test, 1741, 1)\n",
    "reshape(X_court_test, 1741, 19)\n",
    "\n",
    "X_court_train = [X_court_train[:,1:19];X_court_valid[:,1:19]]\n",
    "y_court_train = [y_court_train[:];y_court_valid[:]];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# office init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_office_train = office_train[:Office_y]\n",
    "y_office_train = convert(Array, y_office_train)\n",
    "X_office_train = office_train[1:19]\n",
    "X_office_train = convert(Array,X_office_train)\n",
    "reshape(y_office_train, 5227, 1)\n",
    "reshape(X_office_train, 5227, 19)\n",
    "\n",
    "y_office_valid = office_valid[:Office_y]\n",
    "y_office_valid = convert(Array, y_office_valid)\n",
    "X_office_valid = office_valid[1:19]\n",
    "X_office_valid = convert(Array,X_office_valid)\n",
    "reshape(y_office_valid, 1742, 1)\n",
    "reshape(X_office_valid, 1742, 19)\n",
    "\n",
    "y_office_test = office_test[:Office_y]\n",
    "y_office_test = convert(Array, y_office_test)\n",
    "X_office_test = office_test[1:19]\n",
    "X_office_test = convert(Array,X_office_test)\n",
    "reshape(y_office_test, 1741, 1)\n",
    "reshape(X_office_test, 1741, 19)\n",
    "\n",
    "X_office_train = [X_office_train[:,1:19];X_office_valid[:,1:19]]\n",
    "y_office_train = [y_office_train[:];y_office_valid[:]];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# school1 init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_school1_train = school1_train[:School1_y]\n",
    "y_school1_train = convert(Array, y_school1_train)\n",
    "X_school1_train = school1_train[1:19]\n",
    "X_school1_train = convert(Array,X_school1_train)\n",
    "reshape(y_school1_train, 5227, 1)\n",
    "reshape(X_school1_train, 5227, 19)\n",
    "\n",
    "y_school1_valid = school1_valid[:School1_y]\n",
    "y_school1_valid = convert(Array, y_school1_valid)\n",
    "X_school1_valid = school1_valid[1:19]\n",
    "X_school1_valid = convert(Array,X_school1_valid)\n",
    "reshape(y_school1_valid, 1742, 1)\n",
    "reshape(X_school1_valid, 1742, 19)\n",
    "\n",
    "y_school1_test = school1_test[:School1_y]\n",
    "y_school1_test = convert(Array, y_school1_test)\n",
    "X_school1_test = school1_test[1:19]\n",
    "X_school1_test = convert(Array,X_school1_test)\n",
    "reshape(y_school1_test, 1741, 1)\n",
    "reshape(X_school1_test, 1741, 19)\n",
    "\n",
    "X_school1_train = [X_school1_train[:,1:19];X_school1_valid[:,1:19]]\n",
    "y_school1_train = [y_school1_train[:];y_school1_valid[:]];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# school2 init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_school2_train = school2_train[:School2_y]\n",
    "y_school2_train = convert(Array, y_school2_train)\n",
    "X_school2_train = school2_train[1:19]\n",
    "X_school2_train = convert(Array,X_school2_train)\n",
    "reshape(y_school2_train, 5227, 1)\n",
    "reshape(X_school2_train, 5227, 19)\n",
    "\n",
    "y_school2_valid = school2_valid[:School2_y]\n",
    "y_school2_valid = convert(Array, y_school2_valid)\n",
    "X_school2_valid = school2_valid[1:19]\n",
    "X_school2_valid = convert(Array,X_school2_valid)\n",
    "reshape(y_school2_valid, 1742, 1)\n",
    "reshape(X_school2_valid, 1742, 19)\n",
    "\n",
    "y_school2_test = school2_test[:School2_y]\n",
    "y_school2_test = convert(Array, y_school2_test)\n",
    "X_school2_test = school2_test[1:19]\n",
    "X_school2_test = convert(Array,X_school2_test)\n",
    "reshape(y_school2_test, 1741, 1)\n",
    "reshape(X_school2_test, 1741, 19)\n",
    "\n",
    "X_school2_train = [X_school2_train[:,1:19];X_school2_valid[:,1:19]]\n",
    "y_school2_train = [y_school2_train[:];y_school2_valid[:]];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accuracy (generic function with 1 method)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy determination\n",
    "\n",
    "function accuracy(yPred, yTrue)\n",
    "    lenY = length(yTrue)\n",
    "    numCorrect = 0\n",
    "    \n",
    "    for i = 1:lenY\n",
    "        if (abs(yPred[i] - yTrue[i])/yTrue[i]) <= 0.1\n",
    "            numCorrect += 1\n",
    "        end\n",
    "    end\n",
    "    return numCorrect/lenY\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit random forest regression model on court"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:26\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 200, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Float64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:96\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:106\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{RandomForest.TreeNode}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:107\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:108\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33m~(A::AbstractArray) is deprecated, use .~(A) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1m~\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Bool,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_tree#88\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Bool, ::Function, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:204\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##generate_tree)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_tree, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:112\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[9], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated by: RandomForest v. 0.0.11\n",
      "Prediction task: regression\n",
      "Learning method:\n",
      "\tlearningType = RandomForest.Regressor()\n",
      "\tmodeltype = forest\n",
      "\tnotrees = 200\n",
      "\tsplit = default\n",
      "\tminleaf = 1\n",
      "\tmaxdepth = 0\n",
      "\trandsub = default\n",
      "\trandval = true\n",
      "\tsplitsample = 0\n",
      "\tbagging = true\n",
      "\tbagsize = 1.0\n",
      "\tmodpred = false\n",
      "\tlaplace = false\n",
      "\tconfidence = 0.95\n",
      "\tconformal = default\n",
      "OOB MSE: 3744.4038376560443\n",
      "Variable importance:\n",
      "x2\t0.6645380528576248\n",
      "x14\t0.06809547599825981\n",
      "x5\t0.048464823676757106\n",
      "x16\t0.04090841366867604\n",
      "x8\t0.033575155198580946\n",
      "x11\t0.02732533552850223\n",
      "x10\t0.02249918629822493\n",
      "x15\t0.01557086580056788\n",
      "x13\t0.014088354927231398\n",
      "x3\t0.01276017693353819\n",
      "x4\t0.011037756512998083\n",
      "x17\t0.008797803906510053\n",
      "x6\t0.0067966005269630096\n",
      "x19\t0.006364749825993339\n",
      "x7\t0.006242626474731787\n",
      "x12\t0.005743580522773086\n",
      "x9\t0.004834069046745865\n",
      "x18\t0.0023569722953210087\n",
      "x1\t0.0\n"
     ]
    }
   ],
   "source": [
    "court_forest = forestRegressor(notrees = 200)\n",
    "RandomForest.fit!(court_forest, X_court_train, y_court_train)\n",
    "describe_model(court_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check RMSE/NRMSE of court model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
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      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
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      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "court_RMSE_train = court_MSE_train ^ (1 / 2) = 31.28628702542373\n",
      "court_RMSE_test = court_MSE_test ^ (1 / 2) = 59.65063702384268\n",
      "accuracy(y_court_train, yp_court_train) = 0.9220835126990959\n",
      "accuracy(y_court_test, yp_court_test) = 0.8144744399770247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[60], in expression starting on line 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8144744399770247"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp_court_train = RandomForest.predict(court_forest, X_court_train)\n",
    "yp_court_test = RandomForest.predict(court_forest, X_court_test)\n",
    "\n",
    "court_MSE_train = sum((yp_court_train - y_court_train).^2)/length(y_court_train)\n",
    "court_MSE_test = sum((yp_court_test - y_court_test).^2)/length(y_court_test)\n",
    "\n",
    "\n",
    "#y_court_train_min = minimum(y_court_train)\n",
    "#y_court_test_min = minimum(y_court_test)\n",
    "#y_court_train_max = maximum(y_court_train)\n",
    "#y_court_test_max = maximum(y_court_test)\n",
    "\n",
    "#y_court_train_mean = mean(y_court_train)\n",
    "#y_court_test_mean = mean(y_court_test)\n",
    "\n",
    "#court_NRMSE1_train = RMSE_train / (y_court_train_max - y_court_train_min)\n",
    "#court_NRMSE1_test = RMSE_test / (y_court_test_max - y_court_test_min)\n",
    "\n",
    "\n",
    "#court_NRMSE2_train = RMSE_train / (y_court_train_mean)\n",
    "#court_NRMSE2_test =  RMSE_test / (y_court_test_mean)\n",
    "\n",
    "\n",
    "#@show MSE_train\n",
    "@show court_RMSE_train = court_MSE_train^(1/2)\n",
    "#@show MSE_test\n",
    "@show court_RMSE_test = court_MSE_test^(1/2)\n",
    "@show accuracy(y_court_train, yp_court_train)\n",
    "@show accuracy(y_court_test, yp_court_test)\n",
    "\n",
    "\n",
    "\n",
    "#@show court_NRMSE1_train\n",
    "#@show court_NRMSE1_test\n",
    "#@show court_NRMSE2_train\n",
    "#@show court_NRMSE2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit random forest regression model on office"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:26\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Float64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:96\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 200, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:106\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{RandomForest.TreeNode}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:107\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:108\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33m~(A::AbstractArray) is deprecated, use .~(A) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1m~\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Bool,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_tree#88\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Bool, ::Function, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:204\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##generate_tree)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_tree, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:112\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[11], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated by: RandomForest v. 0.0.11\n",
      "Prediction task: regression\n",
      "Learning method:\n",
      "\tlearningType = RandomForest.Regressor()\n",
      "\tmodeltype = forest\n",
      "\tnotrees = 200\n",
      "\tsplit = default\n",
      "\tminleaf = 1\n",
      "\tmaxdepth = 0\n",
      "\trandsub = default\n",
      "\trandval = true\n",
      "\tsplitsample = 0\n",
      "\tbagging = true\n",
      "\tbagsize = 1.0\n",
      "\tmodpred = false\n",
      "\tlaplace = false\n",
      "\tconfidence = 0.95\n",
      "\tconformal = default\n",
      "OOB MSE: 417.29611770968427\n",
      "Variable importance:\n",
      "x2\t0.5152195007346856\n",
      "x8\t0.12136619169226287\n",
      "x16\t0.0985358622987766\n",
      "x10\t0.04821393556043588\n",
      "x14\t0.04578118006710457\n",
      "x5\t0.04360809097700507\n",
      "x15\t0.02916103258901829\n",
      "x13\t0.02290868517745242\n",
      "x11\t0.018425115436348526\n",
      "x9\t0.009977222399047705\n",
      "x4\t0.009329761104517961\n",
      "x12\t0.00887806492956488\n",
      "x3\t0.007939445689401765\n",
      "x17\t0.005858211032046114\n",
      "x7\t0.004568307039085745\n",
      "x6\t0.0044567471363513985\n",
      "x19\t0.004216977479056946\n",
      "x18\t0.0015556686578376505\n",
      "x1\t0.0\n"
     ]
    }
   ],
   "source": [
    "office_forest = forestRegressor(notrees=200, maxdepth=0)\n",
    "RandomForest.fit!(office_forest, X_office_train, y_office_train)\n",
    "describe_model(office_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check RMSE/NRMSE of office model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "office_RMSE_train = office_MSE_train ^ (1 / 2) = 9.002622745974675\n",
      "office_RMSE_test = office_MSE_test ^ (1 / 2) = 20.74979072748292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[12], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy(y_office_train, yp_office_train) = 0.998278088678433\n",
      "accuracy(y_office_test, yp_office_test) = 0.9672601952900631\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9672601952900631"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp_office_train = RandomForest.predict(office_forest, X_office_train)\n",
    "yp_office_test = RandomForest.predict(office_forest, X_office_test)\n",
    "\n",
    "office_MSE_train = sum((yp_office_train - y_office_train).^2)/length(y_office_train)\n",
    "office_MSE_test = sum((yp_office_test - y_office_test).^2)/length(y_office_test)\n",
    "\n",
    "\n",
    "#y_office_train_min = minimum(y_office_train)\n",
    "#y_office_test_min = minimum(y_office_test)\n",
    "#y_office_train_max = maximum(y_office_train)\n",
    "#y_office_test_max = maximum(y_office_test)\n",
    "\n",
    "#y_office_train_mean = mean(y_office_train)\n",
    "#y_office_test_mean = mean(y_office_test)\n",
    "\n",
    "#office_NRMSE1_train = RMSE_train / (y_office_train_max - y_office_train_min)\n",
    "#office_NRMSE1_test = RMSE_test / (y_office_test_max - y_office_test_min)\n",
    "\n",
    "\n",
    "#office_NRMSE2_train = RMSE_train / (y_office_train_mean)\n",
    "#office_NRMSE2_test =  RMSE_test / (y_office_test_mean)\n",
    "\n",
    "\n",
    "#@show MSE_train\n",
    "@show office_RMSE_train = office_MSE_train^(1/2)\n",
    "#@show MSE_test\n",
    "@show office_RMSE_test = office_MSE_test^(1/2)\n",
    "@show accuracy(y_office_train, yp_office_train)\n",
    "@show accuracy(y_office_test, yp_office_test)\n",
    "\n",
    "\n",
    "\n",
    "#@show office_NRMSE1_train\n",
    "#@show office_NRMSE1_test\n",
    "#@show office_NRMSE2_train\n",
    "#@show office_NRMSE2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit random forest regression model on school1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:26\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Float64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:96\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:106\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{RandomForest.TreeNode}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:107\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:108\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 200, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "while loading In[13], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33m~(A::AbstractArray) is deprecated, use .~(A) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1m~\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Bool,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_tree#88\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Bool, ::Function, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:204\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##generate_tree)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_tree, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:112\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[13], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated by: RandomForest v. 0.0.11\n",
      "Prediction task: regression\n",
      "Learning method:\n",
      "\tlearningType = RandomForest.Regressor()\n",
      "\tmodeltype = forest\n",
      "\tnotrees = 200\n",
      "\tsplit = default\n",
      "\tminleaf = 1\n",
      "\tmaxdepth = 0\n",
      "\trandsub = default\n",
      "\trandval = true\n",
      "\tsplitsample = 0\n",
      "\tbagging = true\n",
      "\tbagsize = 1.0\n",
      "\tmodpred = false\n",
      "\tlaplace = false\n",
      "\tconfidence = 0.95\n",
      "\tconformal = default\n",
      "OOB MSE: 434.2978673728333\n",
      "Variable importance:\n",
      "x2\t0.4981265681961256\n",
      "x8\t0.12314886694872328\n",
      "x14\t0.07167892843558969\n",
      "x16\t0.06888054027828619\n",
      "x5\t0.0441727494834696\n",
      "x15\t0.041069532992518816\n",
      "x10\t0.031098290868443564\n",
      "x13\t0.026167527461680194\n",
      "x11\t0.02514962162849881\n",
      "x17\t0.01617364487732331\n",
      "x9\t0.012554989263505822\n",
      "x19\t0.011748505682501105\n",
      "x12\t0.007203244850946493\n",
      "x7\t0.006164729528363936\n",
      "x6\t0.005169356097315225\n",
      "x4\t0.00432452269576514\n",
      "x3\t0.0039962646234580745\n",
      "x18\t0.0031721160874853055\n",
      "x1\t0.0\n"
     ]
    }
   ],
   "source": [
    "school1_forest = forestRegressor(notrees=200, maxdepth=0)\n",
    "RandomForest.fit!(school1_forest, X_school1_train, y_school1_train)\n",
    "describe_model(school1_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check RMSE/NRMSE of school1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "school1_RMSE_train = school1_MSE_train ^ (1 / 2) = 9.03620100799716\n",
      "school1_RMSE_test = school1_MSE_test ^ (1 / 2) = 20.454527040522912\n",
      "accuracy(y_school1_train, yp_school1_train) = 0.9565217391304348\n",
      "accuracy(y_school1_test, yp_school1_test) = 0.8265364732912119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[57], in expression starting on line 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8265364732912119"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp_school1_train = RandomForest.predict(school1_forest, X_school1_train)\n",
    "yp_school1_test = RandomForest.predict(school1_forest, X_school1_test)\n",
    "\n",
    "school1_MSE_train = sum((yp_school1_train - y_school1_train).^2)/length(y_school1_train)\n",
    "school1_MSE_test = sum((yp_school1_test - y_school1_test).^2)/length(y_school1_test)\n",
    "\n",
    "\n",
    "#y_school1_train_min = minimum(y_school1_train)\n",
    "#y_school1_test_min = minimum(y_school1_test)\n",
    "#y_school1_train_max = maximum(y_school1_train)\n",
    "#y_school1_test_max = maximum(y_school1_test)\n",
    "\n",
    "#y_school1_train_mean = mean(y_school1_train)\n",
    "#y_school1_test_mean = mean(y_school1_test)\n",
    "\n",
    "#school1_NRMSE1_train = RMSE_train / (y_school1_train_max - y_school1_train_min)\n",
    "#school1_NRMSE1_test = RMSE_test / (y_school1_test_max - y_school1_test_min)\n",
    "\n",
    "\n",
    "#school1_NRMSE2_train = RMSE_train / (y_school1_train_mean)\n",
    "#school1_NRMSE2_test =  RMSE_test / (y_school1_test_mean)\n",
    "\n",
    "\n",
    "#@show MSE_train\n",
    "@show school1_RMSE_train = school1_MSE_train^(1/2)\n",
    "#@show MSE_test\n",
    "@show school1_RMSE_test = school1_MSE_test^(1/2)\n",
    "@show accuracy(y_school1_train, yp_school1_train)\n",
    "@show accuracy(y_school1_test, yp_school1_test)\n",
    "\n",
    "\n",
    "\n",
    "#@show school1_NRMSE1_train\n",
    "#@show school1_NRMSE1_test\n",
    "#@show school1_NRMSE2_train\n",
    "#@show school1_NRMSE2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit random forest regression model on school2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:26\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Float64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:96\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 200, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:106\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [15] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [16] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [17] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{RandomForest.TreeNode}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:107\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:108\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [13] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [15] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [16] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33m~(A::AbstractArray) is deprecated, use .~(A) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1m~\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Bool,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_tree#88\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Bool, ::Function, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:204\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##generate_tree)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_tree, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:112\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Int64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[15], in expression starting on line 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated by: RandomForest v. 0.0.11\n",
      "Prediction task: regression\n",
      "Learning method:\n",
      "\tlearningType = RandomForest.Regressor()\n",
      "\tmodeltype = forest\n",
      "\tnotrees = 200\n",
      "\tsplit = default\n",
      "\tminleaf = 1\n",
      "\tmaxdepth = 0\n",
      "\trandsub = default\n",
      "\trandval = true\n",
      "\tsplitsample = 0\n",
      "\tbagging = true\n",
      "\tbagsize = 1.0\n",
      "\tmodpred = false\n",
      "\tlaplace = false\n",
      "\tconfidence = 0.95\n",
      "\tconformal = default\n",
      "OOB MSE: 890.0678124987846\n",
      "Variable importance:\n",
      "x2\t0.5365628470865982\n",
      "x14\t0.1032121192149776\n",
      "x8\t0.07668275364599561\n",
      "x16\t0.06139012469541706\n",
      "x5\t0.04118737822195737\n",
      "x10\t0.03621767249380694\n",
      "x13\t0.028604612685048447\n",
      "x15\t0.024170016455496383\n",
      "x11\t0.015949774520748985\n",
      "x7\t0.015031699448574309\n",
      "x12\t0.013837138069450396\n",
      "x17\t0.010764338699438141\n",
      "x4\t0.008835634563801454\n",
      "x19\t0.008081572568339315\n",
      "x3\t0.007708020770509278\n",
      "x9\t0.0050354011101539685\n",
      "x6\t0.004031508087783418\n",
      "x18\t0.0026973876619031134\n",
      "x1\t0.0\n"
     ]
    }
   ],
   "source": [
    "school2_forest = forestRegressor(notrees=200, maxdepth=0)\n",
    "RandomForest.fit!(school2_forest, X_school2_train, y_school2_train)\n",
    "describe_model(school2_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check RMSE/NRMSE of school2 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 1\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "school2_RMSE_train = school2_MSE_train ^ (1 / 2) = 13.033449407404675\n",
      "school2_RMSE_test = school2_MSE_test ^ (1 / 2) = 29.6356853912629\n",
      "accuracy(y_school2_train, yp_school2_train) = 0.9329889510690199\n",
      "accuracy(y_school2_test, yp_school2_test) = 0.7920735209649626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[16], in expression starting on line 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7920735209649626"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp_school2_train = RandomForest.predict(school2_forest, X_school2_train)\n",
    "yp_school2_test = RandomForest.predict(school2_forest, X_school2_test)\n",
    "\n",
    "school2_MSE_train = sum((yp_school2_train - y_school2_train).^2)/length(y_school2_train)\n",
    "school2_MSE_test = sum((yp_school2_test - y_school2_test).^2)/length(y_school2_test)\n",
    "\n",
    "\n",
    "#y_school2_train_min = minimum(y_school2_train)\n",
    "#y_school2_test_min = minimum(y_school2_test)\n",
    "#y_school2_train_max = maximum(y_school2_train)\n",
    "#y_school2_test_max = maximum(y_school2_test)\n",
    "\n",
    "#y_school2_train_mean = mean(y_school2_train)\n",
    "#y_school2_test_mean = mean(y_school2_test)\n",
    "\n",
    "#school2_NRMSE1_train = RMSE_train / (y_school2_train_max - y_school2_train_min)\n",
    "#school2_NRMSE1_test = RMSE_test / (y_school2_test_max - y_school2_test_min)\n",
    "\n",
    "\n",
    "#school2_NRMSE2_train = RMSE_train / (y_school2_train_mean)\n",
    "#school2_NRMSE2_test =  RMSE_test / (y_school2_test_mean)\n",
    "\n",
    "\n",
    "#@show MSE_train\n",
    "@show school2_RMSE_train = school2_MSE_train^(1/2)\n",
    "#@show MSE_test\n",
    "@show school2_RMSE_test = school2_MSE_test^(1/2)\n",
    "@show accuracy(y_school2_train, yp_school2_train)\n",
    "@show accuracy(y_school2_test, yp_school2_test)\n",
    "\n",
    "\n",
    "\n",
    "#@show school2_NRMSE1_train\n",
    "#@show school2_NRMSE1_test\n",
    "#@show school2_NRMSE2_train\n",
    "#@show school2_NRMSE2_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# do models generalize to each others' data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE_s1_s2 = sum((school1_on_school2 - y_school2_test) .^ 2 / length(y_school2_test)) ^ (1 / 2) = 97.79540222565609\n",
      "accuracy(school1_on_school2, y_school2_test) = 0.20792647903503733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
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      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [10] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[17], in expression starting on line 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.20792647903503733"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# School1 model to predict school2 data\n",
    "\n",
    "school1_on_school2 = RandomForest.predict(school1_forest, X_school2_test)\n",
    "@show RMSE_s1_s2 = sum(((school1_on_school2 - y_school2_test).^2)/length(y_school2_test))^(1/2)\n",
    "@show accuracy(school1_on_school2, y_school2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
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      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:58\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [6] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [7] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [9] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[18], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:576\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[18], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
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      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[18], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:577\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [7] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[18], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
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      "while loading In[18], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
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      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
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      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
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      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
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      "while loading In[18], in expression starting on line 3\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE_s2_s1 = sum((school2_on_school1 - y_school1_test) .^ 2 / length(y_school1_test)) ^ (1 / 2) = 85.12385590708772\n",
      "accuracy(school2_on_school1, y_school1_test) = 0.10913268236645605\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
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      " [9] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
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      " [11] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[18], in expression starting on line 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10913268236645605"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# School2 model to predict school1 data\n",
    "\n",
    "school2_on_school1 = RandomForest.predict(school2_forest, X_school1_test)\n",
    "@show RMSE_s2_s1 = sum(((school2_on_school1 - y_school1_test).^2)/length(y_school1_test))^(1/2)\n",
    "@show accuracy(school2_on_school1, y_school1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "court_RMSE_train = court_MSE_train ^ (1 / 2) = 26.73629709422231\n",
      "court_RMSE_test = court_MSE_test ^ (1 / 2) = 56.04603994750592\n",
      "accuracy(y_court_train, yp_court_train) = 0.9387286554742431\n",
      "accuracy(y_court_test, yp_court_test) = 0.8225157955198162\n",
      "office_RMSE_train = office_MSE_train ^ (1 / 2) = 9.002622745974675\n",
      "office_RMSE_test = office_MSE_test ^ (1 / 2) = 20.74979072748292\n",
      "accuracy(y_office_train, yp_office_train) = 0.998278088678433\n",
      "accuracy(y_office_test, yp_office_test) = 0.9672601952900631\n",
      "school1_RMSE_train = school1_MSE_train ^ (1 / 2) = 9.03620100799716\n",
      "school1_RMSE_test = school1_MSE_test ^ (1 / 2) = 20.454527040522912\n",
      "accuracy(y_school1_train, yp_school1_train) = 0.9565217391304348\n",
      "accuracy(y_school1_test, yp_school1_test) = 0.8265364732912119\n",
      "school2_RMSE_train = school2_MSE_train ^ (1 / 2) = 13.033449407404675\n",
      "school2_RMSE_test = school2_MSE_test ^ (1 / 2) = 29.6356853912629\n",
      "accuracy(y_school2_train, yp_school2_train) = 0.9329889510690199\n",
      "accuracy(y_school2_test, yp_school2_test) = 0.7920735209649626\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7920735209649626"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@show court_RMSE_train = court_MSE_train^(1/2)\n",
    "@show court_RMSE_test = court_MSE_test^(1/2)\n",
    "@show accuracy(y_court_train, yp_court_train)\n",
    "@show accuracy(y_court_test, yp_court_test)\n",
    "\n",
    "@show office_RMSE_train = office_MSE_train^(1/2)\n",
    "@show office_RMSE_test = office_MSE_test^(1/2)\n",
    "@show accuracy(y_office_train, yp_office_train)\n",
    "@show accuracy(y_office_test, yp_office_test)\n",
    "\n",
    "@show school1_RMSE_train = school1_MSE_train^(1/2)\n",
    "@show school1_RMSE_test = school1_MSE_test^(1/2)\n",
    "@show accuracy(y_school1_train, yp_school1_train)\n",
    "@show accuracy(y_school1_test, yp_school1_test)\n",
    "\n",
    "@show school2_RMSE_train = school2_MSE_train^(1/2)\n",
    "@show school2_RMSE_test = school2_MSE_test^(1/2)\n",
    "@show accuracy(y_school2_train, yp_school2_train)\n",
    "@show accuracy(y_school2_test, yp_school2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Future}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1minitiate_workers\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:1043\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:26\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [8] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [11] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [12] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Float64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:96\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [13] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array{Int64,1}}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:123\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [14] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [16] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [17] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [18] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [19] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mfind_missing_values\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:124\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:105\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [14] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [16] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [17] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [18] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [19] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mtransform_nonmissing_columns_to_arrays\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Symbol,1}, ::DataFrames.DataFrame, ::Array{Array{Int64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:145\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:106\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [14] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [16] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [17] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [18] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [19] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{RandomForest.TreeNode}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:107\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [13] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Array}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:108\u001b[22m\u001b[22m\n",
      " [4] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [5] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [7] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [8] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [9] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [10] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 10, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [13] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [14] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [15] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [16] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [17] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [18] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33m~(A::AbstractArray) is deprecated, use .~(A) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1m~\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Bool,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#generate_tree#88\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Bool, ::Function, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:204\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##generate_tree)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_tree, ::RandomForest.LearningMethod{RandomForest.Regressor}, ::Array{Int64,1}, ::Array{Float64,1}, ::Array{Float64,1}, ::Array{Any,1}, ::Array{Any,1}, ::Array{Array,1}, ::Array{Symbol,1}, ::Array{Symbol,1}, ::Array{Array{Float64,1},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#generate_trees#87\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::DataFrames.DataFrame, ::Array{Any,1}, ::Bool, ::Function, ::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:112\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mgenerate_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:89\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mbroadcast_t\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Type{Any}, ::Tuple{Base.OneTo{Int64}}, ::CartesianRange{CartesianIndex{1}}, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:256\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mbroadcast_c\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:319\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1mbroadcast\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Function, ::Array{Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Int64,1},Int64,Int64},1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./broadcast.jl:434\u001b[22m\u001b[22m\n",
      " [10] \u001b[1m#generate_model#267\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.LearningMethod{RandomForest.Regressor}, ::Function\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/RandomForest.jl:964\u001b[22m\u001b[22m\n",
      " [11] \u001b[1m(::RandomForest.#kw##generate_model)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#generate_model\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:27\u001b[22m\u001b[22m\n",
      " [13] \u001b[1mfit!\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}, ::Array{Float64,1}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:49\u001b[22m\u001b[22m\n",
      " [14] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:9\u001b[22m\u001b[22m [inlined]\n",
      " [15] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [16] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [17] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [18] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [19] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [20] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:580\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:10\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [11] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [13] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [14] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n",
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Float64}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1mapply_trees\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Tuple{RandomForest.LearningMethod{RandomForest.Regressor},Array{Any,1},Array{RandomForest.TreeNode,1}}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:581\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:529\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [6] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [8] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:10\u001b[22m\u001b[22m [inlined]\n",
      " [9] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [11] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [12] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [13] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [14] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[33mWARNING: \u001b[39m\u001b[22m\u001b[33mArray{T}(::Type{T}, m::Int) is deprecated, use Array{T}(m) instead.\u001b[39m\n",
      "Stacktrace:\n",
      " [1] \u001b[1mdepwarn\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::Symbol\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:70\u001b[22m\u001b[22m\n",
      " [2] \u001b[1mArray\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Type{Tuple}, ::Int64\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./deprecated.jl:57\u001b[22m\u001b[22m\n",
      " [3] \u001b[1m#apply_model_internal#91\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Float64, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/regression.jl:545\u001b[22m\u001b[22m\n",
      " [4] \u001b[1m(::RandomForest.#kw##apply_model_internal)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Array{Any,1}, ::RandomForest.#apply_model_internal, ::RandomForest.PredictionModel{RandomForest.Regressor}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./<missing>:0\u001b[22m\u001b[22m\n",
      " [5] \u001b[1m#predict#159\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Colon, ::Function, ::RandomForest.PredictionModel{RandomForest.Regressor}, ::DataFrames.DataFrame\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:59\u001b[22m\u001b[22m\n",
      " [6] \u001b[1mpredict\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::RandomForest.PredictionModel{RandomForest.Regressor}, ::Array{Real,2}\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/RandomForest/src/scikitlearnAPI.jl:68\u001b[22m\u001b[22m\n",
      " [7] \u001b[1mmacro expansion\u001b[22m\u001b[22m at \u001b[1m./In[63]:10\u001b[22m\u001b[22m [inlined]\n",
      " [8] \u001b[1manonymous\u001b[22m\u001b[22m at \u001b[1m./<missing>:?\u001b[22m\u001b[22m\n",
      " [9] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./loading.jl:515\u001b[22m\u001b[22m\n",
      " [10] \u001b[1minclude_string\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Module, ::String, ::String\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/Compat/src/Compat.jl:174\u001b[22m\u001b[22m\n",
      " [11] \u001b[1mexecute_request\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket, ::IJulia.Msg\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/execute_request.jl:154\u001b[22m\u001b[22m\n",
      " [12] \u001b[1meventloop\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::ZMQ.Socket\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m/Users/AlexLeong/.julia/v0.6/IJulia/src/eventloop.jl:8\u001b[22m\u001b[22m\n",
      " [13] \u001b[1m(::IJulia.##14#17)\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./task.jl:335\u001b[22m\u001b[22m\n",
      "while loading In[63], in expression starting on line 6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 20, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "3\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 30, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "4\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 40, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "5\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 50, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "6\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 60, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "7\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 70, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "8\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 80, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "9\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 90, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "10\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 100, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "11\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 110, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "12\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 120, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "13\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 130, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "14\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 140, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "15\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 150, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "16\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 160, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "17\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 170, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "18\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 180, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "19\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 190, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "20\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 200, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "21\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 210, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "22\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 220, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "23\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 230, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "24\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 240, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n",
      "25\n",
      "Method: RandomForest.LearningMethod{RandomForest.Regressor}(RandomForest.Regressor(), :forest, 250, :default, 1, 0, :default, true, 0, true, 1.0, false, false, 0.95, :default)\n"
     ]
    }
   ],
   "source": [
    "# Generate RMSE values for forest sizes 10:10:250 for plotting\n",
    "# Takes several minutes to complete\n",
    "\n",
    "court_forest_stats = [zeros(25) zeros(25) zeros(25) zeros(25)]\n",
    "\n",
    "for i = 1:25\n",
    "    println(i)\n",
    "    court_forest = forestRegressor(notrees = (i*10))\n",
    "    RandomForest.fit!(court_forest, X_court_train, y_court_train)\n",
    "    yp_court_train = RandomForest.predict(court_forest, X_court_train)\n",
    "    yp_court_test = RandomForest.predict(court_forest, X_court_test)\n",
    "\n",
    "    court_forest_stats[i,1] = (sum((yp_court_train - y_court_train).^2)/length(y_court_train))^(1/2)\n",
    "    court_forest_stats[i,2] = (sum((yp_court_test - y_court_test).^2)/length(y_court_test))^(1/2)\n",
    "    court_forest_stats[i,3] = accuracy(yp_court_train, y_court_train)\n",
    "    court_forest_stats[i,4] = accuracy(yp_court_test, y_court_test)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25×4 Array{Float64,2}:\n",
       " 31.9136  60.075   0.914909  0.809305\n",
       " 29.3356  59.1658  0.923375  0.801264\n",
       " 28.8771  58.7219  0.917779  0.782883\n",
       " 28.344   57.3498  0.92237   0.796094\n",
       " 28.7664  56.4807  0.923231  0.800115\n",
       " 27.8081  57.7022  0.921797  0.793797\n",
       " 26.7915  56.4443  0.933563  0.808156\n",
       " 27.9601  55.8983  0.931554  0.80471 \n",
       " 27.1538  56.0506  0.932128  0.8139  \n",
       " 27.0851  56.566   0.933419  0.816198\n",
       " 26.8473  56.6387  0.933132  0.808156\n",
       " 27.522   56.4235  0.932989  0.807007\n",
       " 27.1117  56.3739  0.931841  0.815049\n",
       " 26.823   56.5074  0.933276  0.805859\n",
       " 26.7891  55.8259  0.935141  0.815623\n",
       " 26.7089  55.8417  0.933132  0.806433\n",
       " 26.7929  56.4399  0.933993  0.809879\n",
       " 26.7826  55.9539  0.935572  0.819644\n",
       " 26.8769  56.2526  0.934854  0.815049\n",
       " 26.7531  55.9678  0.933276  0.820218\n",
       " 26.2949  56.2512  0.935141  0.814474\n",
       " 26.9167  55.9577  0.936146  0.8139  \n",
       " 26.8541  55.6575  0.934424  0.812177\n",
       " 27.4019  56.4937  0.93385   0.811028\n",
       " 26.6232  55.8139  0.931124  0.809305"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "court_forest_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x16b69ab90>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x16beef610>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "plot([10,20,30,40,50,60,70,80,90,100,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260], court_forest_stats[:,1].^2, c=\"orange\", label=\"Training data\", linewidth=3)\n",
    "plot([10,20,30,40,50,60,70,80,90,100,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260], court_forest_stats[:,2].^2, c=\"b\", label=\"Test Data\", linewidth=3)\n",
    "xlabel(\"Forest Size\")\n",
    "ylabel(\"MSE\")\n",
    "title(\"Random Forest Regression\")\n",
    "legend(prop=Dict(\"size\"=>12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x16c24d290>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x16c398f10>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "plot([10,20,30,40,50,60,70,80,90,100,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260], court_forest_stats[:,3], c=\"r\", label=\"Training data\", linewidth=3)\n",
    "plot([10,20,30,40,50,60,70,80,90,100,120,130,140,150,160,170,180,190,200,210,220,230,240,250,260], court_forest_stats[:,4], c=\"g\", label=\"Test Data\", linewidth=3)\n",
    "xlabel(\"Forest Size\")\n",
    "ylabel(\"percent\")\n",
    "title(\"Random Forest Regression\")\n",
    "legend(prop=Dict(\"size\"=>12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.6.0",
   "language": "julia",
   "name": "julia-0.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}