LightGBM API walkthrough and a discussion about categorical features in tree-based models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Import-modules\" data-toc-modified-id=\"Import-modules-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Import modules</a></span></li><li><span><a href=\"#LightGBM\" data-toc-modified-id=\"LightGBM-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>LightGBM</a></span></li><li><span><a href=\"#Data-Preprocessing\" data-toc-modified-id=\"Data-Preprocessing-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Data Preprocessing</a></span></li><li><span><a href=\"#Benchmarking\" data-toc-modified-id=\"Benchmarking-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Benchmarking</a></span></li><li><span><a href=\"#Categorical-Variables-in-Tree-based-Models\" data-toc-modified-id=\"Categorical-Variables-in-Tree-based-Models-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Categorical Variables in Tree-based Models</a></span></li><li><span><a href=\"#References\" data-toc-modified-id=\"References-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>References</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<font color=black>\n",
    "\n",
    "**What?** LightGBM API walkthrough and a discussion about categorical features in tree-based models\n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import modules\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:37.965563Z",
     "start_time": "2020-10-27T23:50:36.958947Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import time\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from lightgbm import plot_importance\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Gradient boosting](http://nbviewer.jupyter.org/github/ethen8181/machine-learning/blob/master/trees/gbm/gbm.ipynb) is a machine learning technique that produces a prediction model in the form of an ensemble of weak classifiers, optimizing for a differentiable loss function. One of the most popular types of gradient boosting is gradient boosted trees, that internally is made up of an ensemble of week [decision trees](http://nbviewer.jupyter.org/github/ethen8181/machine-learning/blob/master/trees/decision_tree.ipynb). There are two different ways to compute the trees: level-wise and leaf-wise as illustrated by the diagram below:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "> The level-wise strategy adds complexity extending the depth of the tree level by level. As a contrary, the leaf-wise strategy generates branches by optimizing a loss.\n",
    "\n",
    "The level-wise strategy grows the tree level by level. In this strategy, each node splits the data prioritizing the nodes closer to the tree root. The leaf-wise strategy grows the tree by splitting the data at the nodes with the highest loss change. Level-wise growth is usually better for smaller datasets whereas leaf-wise tends to overfit. Leaf-wise growth tends to [excel in larger datasets](http://researchcommons.waikato.ac.nz/handle/10289/2317) where it is considerably faster than level-wise growth.\n",
    "\n",
    "A key challenge in training boosted decision trees is the [computational cost of finding the best split](https://arxiv.org/abs/1706.08359) for each leaf. Conventional techniques find the [exact split](https://arxiv.org/abs/1603.02754) for each leaf, and require scanning through all the data in each iteration. A different approach [approximates the split](https://arxiv.org/abs/1611.01276) by building histograms of the features. That way, the algorithm doesn’t need to evaluate every single value of the features to compute the split, but only the bins of the histogram, which are bounded. This approach turns out to be much more efficient for large datasets, without adversely affecting accuracy.\n",
    "\n",
    "With all of that being said LightGBM is a fast, distributed, high performance gradient boosting that was open-source by Microsoft around  August 2016. The main advantages of LightGBM includes:\n",
    "\n",
    "- Faster training speed and higher efficiency: LightGBM use histogram based algorithm i.e it buckets continuous feature values into discrete bins which fasten the training procedure.\n",
    "- Lower memory usage: Replaces continuous values to discrete bins which result in lower memory usage.\n",
    "- Better accuracy than any other boosting algorithm: It produces much more complex trees by following leaf wise split approach rather than a level-wise approach which is the main factor in achieving higher accuracy. However, it can sometimes lead to overfitting which can be avoided by setting the max_depth parameter.\n",
    "- Compatibility with Large Datasets: It is capable of performing equally good with large datasets with a significant reduction in training time as compared to XGBoost.\n",
    "- Parallel learning supported.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook compares LightGBM with [XGBoost](http://nbviewer.jupyter.org/github/ethen8181/machine-learning/blob/master/trees/xgboost.ipynb), another extremely popular gradient boosting framework by applying both the algorithms to a dataset and then comparing the model's performance and execution time. Here we will be using the [Adult dataset](http://archive.ics.uci.edu/ml/datasets/Adult) that consists of 32561 observations and 14 features describing individuals from various countries. Our target is to predict whether a person makes <=50k or >50k annually on basis of the other information available. Dataset consists of 32561 observations and 14 features describing individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:41.005963Z",
     "start_time": "2020-10-27T23:50:40.817642Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimensions: (32561, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education_num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital_status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week native_country income  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_data():\n",
    "    file_path = 'adult.csv'\n",
    "    if not os.path.isfile(file_path):\n",
    "        def chunks(input_list, n_chunk):\n",
    "            \"\"\"take a list and break it up into n-size chunks\"\"\"\n",
    "            for i in range(0, len(input_list), n_chunk):\n",
    "                yield input_list[i:i + n_chunk]\n",
    "\n",
    "        columns = [\n",
    "            'age',\n",
    "            'workclass',\n",
    "            'fnlwgt',\n",
    "            'education',\n",
    "            'education_num',\n",
    "            'marital_status',\n",
    "            'occupation',\n",
    "            'relationship',\n",
    "            'race',\n",
    "            'sex',\n",
    "            'capital_gain',\n",
    "            'capital_loss',\n",
    "            'hours_per_week',\n",
    "            'native_country',\n",
    "            'income'\n",
    "        ]\n",
    "\n",
    "        url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\n",
    "        r = requests.get(url)\n",
    "        raw_text = r.text.replace('\\n', ',')\n",
    "        splitted_text = re.split(r',\\s*', raw_text)\n",
    "        data = list(chunks(splitted_text, n_chunk=len(columns)))\n",
    "        data = pd.DataFrame(data, columns=columns).dropna(axis=0, how='any')\n",
    "        data.to_csv(file_path, index=False)\n",
    "\n",
    "    data = pd.read_csv(file_path)\n",
    "    return data\n",
    "\n",
    "\n",
    "data = get_data()\n",
    "print('dimensions:', data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:42.865677Z",
     "start_time": "2020-10-27T23:50:42.826673Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of numerical features:  6\n",
      "number of categorical features:  8\n"
     ]
    }
   ],
   "source": [
    "label_col = 'income'\n",
    "cat_cols = [\n",
    "    'workclass',\n",
    "    'education',\n",
    "    'marital_status',\n",
    "    'occupation',\n",
    "    'relationship',\n",
    "    'race',\n",
    "    'sex',\n",
    "    'native_country'\n",
    "]\n",
    "\n",
    "num_cols = [\n",
    "    'age',\n",
    "    'fnlwgt',\n",
    "    'education_num',\n",
    "    'capital_gain',\n",
    "    'capital_loss',\n",
    "    'hours_per_week'\n",
    "]\n",
    "\n",
    "print('number of numerical features: ', len(num_cols))\n",
    "print('number of categorical features: ', len(cat_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:44.839694Z",
     "start_time": "2020-10-27T23:50:44.780245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels distribution: [0.75919044 0.24080956]\n"
     ]
    }
   ],
   "source": [
    "label_encode = LabelEncoder()\n",
    "data[label_col] = label_encode.fit_transform(data[label_col])\n",
    "y = data[label_col].values\n",
    "data = data.drop(label_col, axis=1)\n",
    "\n",
    "print('labels distribution:', np.bincount(y) / y.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:45.832667Z",
     "start_time": "2020-10-27T23:50:45.757734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimensions: (29304, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>Private</td>\n",
       "      <td>168381</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>134613</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>68678</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>110871</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>?</td>\n",
       "      <td>117528</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt  education  education_num      marital_status  \\\n",
       "0   52   Private  168381    HS-grad              9             Widowed   \n",
       "1   31   Private  134613  Assoc-voc             11  Married-civ-spouse   \n",
       "2   22   Private   68678    HS-grad              9  Married-civ-spouse   \n",
       "3   55   Private  110871  Bachelors             13  Married-civ-spouse   \n",
       "4   35         ?  117528  Assoc-voc             11  Married-civ-spouse   \n",
       "\n",
       "        occupation relationship                race     sex  capital_gain  \\\n",
       "0    Other-service    Unmarried  Asian-Pac-Islander  Female             0   \n",
       "1  Exec-managerial         Wife               Black  Female             0   \n",
       "2            Sales      Husband               Black    Male             0   \n",
       "3            Sales      Husband               White    Male             0   \n",
       "4                ?         Wife               White  Female             0   \n",
       "\n",
       "   capital_loss  hours_per_week native_country  \n",
       "0             0              40          India  \n",
       "1             0              43  United-States  \n",
       "2             0              40  United-States  \n",
       "3             0              40  United-States  \n",
       "4             0              40  United-States  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size = 0.1\n",
    "split_random_state = 1234\n",
    "df_train, df_test, y_train, y_test = train_test_split(\n",
    "    data, y, test_size=test_size,\n",
    "    random_state=split_random_state, stratify=y)\n",
    "\n",
    "df_train = df_train.reset_index(drop=True)\n",
    "df_test = df_test.reset_index(drop=True)\n",
    "\n",
    "print('dimensions:', df_train.shape)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll perform very little feature engineering as that's not our main focus here. The following code chunk only one hot encodes the categorical features. There will be follow up discussions on this in later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:47.585465Z",
     "start_time": "2020-10-27T23:50:47.528126Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of one hot encoded categorical columns:  102\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['workclass_?', 'workclass_Federal-gov', 'workclass_Local-gov',\n",
       "       'workclass_Never-worked', 'workclass_Private'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "one_hot_encoder = OneHotEncoder(sparse=False, dtype=np.int32)\n",
    "one_hot_encoder.fit(df_train[cat_cols])\n",
    "cat_one_hot_cols = one_hot_encoder.get_feature_names(cat_cols)\n",
    "\n",
    "print('number of one hot encoded categorical columns: ', len(cat_one_hot_cols))\n",
    "cat_one_hot_cols[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:48.602382Z",
     "start_time": "2020-10-27T23:50:48.575959Z"
    }
   },
   "outputs": [],
   "source": [
    "def preprocess_one_hot(df, one_hot_encoder, num_cols, cat_cols):\n",
    "    df = df.copy()\n",
    "    \n",
    "    cat_one_hot_cols = one_hot_encoder.get_feature_names(cat_cols)\n",
    "\n",
    "    df_one_hot = pd.DataFrame(\n",
    "        one_hot_encoder.transform(df[cat_cols]),\n",
    "        columns=cat_one_hot_cols\n",
    "    )\n",
    "    df_preprocessed = pd.concat([\n",
    "        df[num_cols],\n",
    "        df_one_hot\n",
    "    ], axis=1)\n",
    "    return df_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:50:50.316343Z",
     "start_time": "2020-10-27T23:50:50.188666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29304, 108)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age                               int64\n",
       "fnlwgt                            int64\n",
       "education_num                     int64\n",
       "capital_gain                      int64\n",
       "capital_loss                      int64\n",
       "                                  ...  \n",
       "native_country_Thailand           int32\n",
       "native_country_Trinadad&Tobago    int32\n",
       "native_country_United-States      int32\n",
       "native_country_Vietnam            int32\n",
       "native_country_Yugoslavia         int32\n",
       "Length: 108, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_one_hot = preprocess_one_hot(df_train, one_hot_encoder, num_cols, cat_cols)\n",
    "df_test_one_hot = preprocess_one_hot(df_test, one_hot_encoder, num_cols, cat_cols)\n",
    "print(df_train_one_hot.shape)\n",
    "df_train_one_hot.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarking\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next section compares the xgboost and lightgbm's implementation in terms of both execution time and model performance. There are a bunch of other hyperparameters that we as the end-user can specify, but here we explicity specify arguably the most important ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:02.266532Z",
     "start_time": "2020-10-27T23:50:56.897609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n",
      "elapse:,  0.327070951461792\n"
     ]
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "\n",
    "lgb = LGBMClassifier(\n",
    "    n_jobs=-1,\n",
    "    max_depth=6,\n",
    "    subsample=1,\n",
    "    n_estimators=100,\n",
    "    learning_rate=0.1,\n",
    "    colsample_bytree=1,\n",
    "    objective='binary',\n",
    "    boosting_type='gbdt')\n",
    "\n",
    "start = time.time()\n",
    "lgb.fit(df_train_one_hot, y_train)\n",
    "lgb_elapse = time.time() - start\n",
    "print('elapse:, ', lgb_elapse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:24.890527Z",
     "start_time": "2020-10-27T23:51:05.413283Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapse:,  14.434311866760254\n"
     ]
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "\n",
    "# raw xgboost\n",
    "xgb = XGBClassifier(\n",
    "    n_jobs=-1,\n",
    "    max_depth=6,\n",
    "    subsample=1,\n",
    "    n_estimators=100,\n",
    "    learning_rate=0.1,\n",
    "    colsample_bytree=1,\n",
    "    objective='binary:logistic',\n",
    "    booster='gbtree')\n",
    "\n",
    "start = time.time()\n",
    "xgb.fit(df_train_one_hot, y_train)\n",
    "xgb_elapse = time.time() - start\n",
    "print('elapse:, ', xgb_elapse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost includes a `tree_method = 'hist' `option that buckets continuous variables into bins to speed up training, we also set `grow_policy = 'lossguide'` to favor splitting at nodes with highest loss change, which mimics LightGBM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:35.456722Z",
     "start_time": "2020-10-27T23:51:29.605094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapse:,  0.8119819164276123\n"
     ]
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "\n",
    "xgb_hist = XGBClassifier(\n",
    "    n_jobs=-1,\n",
    "    max_depth=6,\n",
    "    subsample=1,\n",
    "    n_estimators=100,\n",
    "    learning_rate=0.1,\n",
    "    colsample_bytree=1,\n",
    "    objective='binary:logistic',\n",
    "    booster='gbtree',\n",
    "    tree_method='hist',\n",
    "    grow_policy='lossguide')\n",
    "\n",
    "start = time.time()\n",
    "xgb_hist.fit(df_train_one_hot, y_train)\n",
    "xgb_hist_elapse = time.time() - start\n",
    "print('elapse:, ', xgb_hist_elapse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:38.783066Z",
     "start_time": "2020-10-27T23:51:38.655229Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc score:  0.9352834593198379\n",
      "auc score:  0.9348347355521263\n",
      "auc score:  0.9351145431888891\n"
     ]
    }
   ],
   "source": [
    "# evaluate performance\n",
    "y_pred = lgb.predict_proba(df_test_one_hot)[:, 1]\n",
    "lgb_auc = roc_auc_score(y_test, y_pred)\n",
    "print('auc score: ', lgb_auc)\n",
    "\n",
    "y_pred = xgb.predict_proba(df_test_one_hot)[:, 1]\n",
    "xgb_auc = roc_auc_score(y_test, y_pred)\n",
    "print('auc score: ', xgb_auc)\n",
    "\n",
    "y_pred = xgb_hist.predict_proba(df_test_one_hot)[:, 1]\n",
    "xgb_hist_auc = roc_auc_score(y_test, y_pred)\n",
    "print('auc score: ', xgb_hist_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:41.434248Z",
     "start_time": "2020-10-27T23:51:41.398426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapse_time</th>\n",
       "      <th>auc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LightGBM</th>\n",
       "      <td>0.327071</td>\n",
       "      <td>0.935283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoostHist</th>\n",
       "      <td>0.811982</td>\n",
       "      <td>0.935115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>14.434312</td>\n",
       "      <td>0.934835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             elapse_time  auc_score\n",
       "LightGBM        0.327071   0.935283\n",
       "XGBoostHist     0.811982   0.935115\n",
       "XGBoost        14.434312   0.934835"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparison table\n",
    "results = pd.DataFrame({\n",
    "    'elapse_time': [lgb_elapse, xgb_hist_elapse, xgb_elapse],\n",
    "    'auc_score': [lgb_auc, xgb_hist_auc, xgb_auc]})\n",
    "results.index = ['LightGBM', 'XGBoostHist', 'XGBoost']\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the resulting table, we can see that there isn't a noticeable difference in auc score between the two implementations. On the other hand, there is a significant difference in the time it takes to finish the whole training procedure. This is a huge advantage and makes LightGBM a much better approach when dealing with large datasets.\n",
    "\n",
    "For those interested, the people at Microsoft has a blog that has a even more thorough benchmark result on various datasets. Link is included below along with a summary of their results:\n",
    "\n",
    "> [Blog: Lessons Learned From Benchmarking Fast Machine Learning Algorithms](https://blogs.technet.microsoft.com/machinelearning/2017/07/25/lessons-learned-benchmarking-fast-machine-learning-algorithms/)\n",
    ">\n",
    "> Our results, based on tests on six datasets, are summarized as follows:\n",
    "\n",
    "> - XGBoost and LightGBM achieve similar accuracy metrics.\n",
    "> - LightGBM has lower training time than XGBoost and its histogram-based variant, XGBoost hist, for all test datasets, on both CPU and GPU implementations. The training time difference between the two libraries depends on the dataset, and can be as big as 25 times.\n",
    "> - XGBoost GPU implementation does not scale well to large datasets and ran out of memory in half of the tests.\n",
    "> - XGBoost hist may be significantly slower than the original XGBoost when feature dimensionality is high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical Variables in Tree-based Models\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many real-world datasets include a mix of continuous and categorical variables. The property of the latter is that their values has zero inherent ordering. One major advantage of decision tree models and their ensemble counterparts, such as random forests, extra trees and gradient boosted trees, is that they are able to operate on both continuous and categorical variables directly (popular implementations of tree-based models differ as to whether they honor this fact). In contrast, most other popular models (e.g., generalized linear models, neural networks) must instead transform categorical variables into some numerical format, usually by one-hot encoding them to create a new dummy variable for each level of the original variable. e.g.\n",
    "\n",
    "One drawback of one hot encoding is that they can lead to a huge increase in the dimensionality of the feature representations. For example, one hot encoding U.S. states adds 49 dimensions to to our feature representation.\n",
    "\n",
    "To understand why we don't need to perform one hot encoding for tree-based models, we need to refer back to the logic of tree-based algorithms. At the heart of the tree-based algorithm is a sub-algorithm that splits the samples into two bins by selecting a feature and a value. This splitting algorithm considers each of the features in turn, and for each feature selects the value of that feature that minimizes the impurity of the bins.\n",
    "\n",
    "This means tree-based models are essentially looking for places to split the data, they are not multiplying our inputs by weights. In contrast, most other popular models (e.g., generalized linear models, neural networks) would interpret categorical variables such as red=1, blue=2 as blue is twice the amount of red, which is obviously not what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:46.390899Z",
     "start_time": "2020-10-27T23:51:46.332767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrdinalEncoder(dtype=<class 'numpy.int32'>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_encoder = OrdinalEncoder(dtype=np.int32)\n",
    "ordinal_encoder.fit(df_train[cat_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:47.827221Z",
     "start_time": "2020-10-27T23:51:47.800347Z"
    }
   },
   "outputs": [],
   "source": [
    "def preprocess_ordinal(df, ordinal_encoder, cat_cols, cat_dtype='int32'):\n",
    "    df = df.copy()\n",
    "    df[cat_cols] = ordinal_encoder.transform(df[cat_cols])\n",
    "    df[cat_cols] = df[cat_cols].astype(cat_dtype)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:51:49.413004Z",
     "start_time": "2020-10-27T23:51:49.257154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29304, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age               int64\n",
       "workclass         int32\n",
       "fnlwgt            int64\n",
       "education         int32\n",
       "education_num     int64\n",
       "marital_status    int32\n",
       "occupation        int32\n",
       "relationship      int32\n",
       "race              int32\n",
       "sex               int32\n",
       "capital_gain      int64\n",
       "capital_loss      int64\n",
       "hours_per_week    int64\n",
       "native_country    int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_ordinal = preprocess_ordinal(df_train, ordinal_encoder, cat_cols)\n",
    "df_test_ordinal = preprocess_ordinal(df_test, ordinal_encoder, cat_cols)\n",
    "print(df_train_ordinal.shape)\n",
    "df_train_ordinal.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:52:00.478855Z",
     "start_time": "2020-10-27T23:51:55.116271Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves\n",
      "elapse:,  0.2872467041015625\n",
      "auc score:  0.9348548507555065\n"
     ]
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "\n",
    "lgb = LGBMClassifier(\n",
    "    n_jobs=-1,\n",
    "    max_depth=6,\n",
    "    subsample=1,\n",
    "    n_estimators=100,\n",
    "    learning_rate=0.1,\n",
    "    colsample_bytree=1,\n",
    "    objective='binary',\n",
    "    boosting_type='gbdt')\n",
    "\n",
    "start = time.time()\n",
    "lgb.fit(df_train_ordinal, y_train)\n",
    "lgb_ordinal_elapse = time.time() - start\n",
    "print('elapse:, ', lgb_ordinal_elapse)\n",
    "\n",
    "y_pred = lgb.predict_proba(df_test_ordinal)[:, 1]\n",
    "lgb_ordinal_auc = roc_auc_score(y_test, y_pred)\n",
    "print('auc score: ', lgb_ordinal_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:52:02.600833Z",
     "start_time": "2020-10-27T23:52:02.552515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapse_time</th>\n",
       "      <th>auc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LightGBM Ordinal</th>\n",
       "      <td>0.287247</td>\n",
       "      <td>0.934855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LightGBM</th>\n",
       "      <td>0.327071</td>\n",
       "      <td>0.935283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoostHist</th>\n",
       "      <td>0.811982</td>\n",
       "      <td>0.935115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>14.434312</td>\n",
       "      <td>0.934835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  elapse_time  auc_score\n",
       "LightGBM Ordinal     0.287247   0.934855\n",
       "LightGBM             0.327071   0.935283\n",
       "XGBoostHist          0.811982   0.935115\n",
       "XGBoost             14.434312   0.934835"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparison table\n",
    "results = pd.DataFrame({\n",
    "    'elapse_time': [lgb_ordinal_elapse, lgb_elapse, xgb_hist_elapse, xgb_elapse],\n",
    "    'auc_score': [lgb_ordinal_auc, lgb_auc, xgb_hist_auc, xgb_auc]})\n",
    "results.index = ['LightGBM Ordinal', 'LightGBM', 'XGBoostHist', 'XGBoost']\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result above, we can see that it requires even less training time without sacrificing any sort of performance. What's even more is that we now no longer need to perform the one hot encoding on our categorical features. The code chunk below shows this is highly advantageous from a memory-usage perspective when we have a bunch of categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:52:06.165449Z",
     "start_time": "2020-10-27T23:52:06.120179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OneHot Encoding\n",
      "number of columns:  108\n",
      "memory usage:  13362752\n",
      "\n",
      "Ordinal Encoding\n",
      "number of columns:  14\n",
      "memory usage:  2344448\n"
     ]
    }
   ],
   "source": [
    "print('OneHot Encoding')\n",
    "print('number of columns: ', df_train_one_hot.shape[1])\n",
    "print('memory usage: ', df_train_one_hot.memory_usage(deep=True).sum())\n",
    "print()\n",
    "\n",
    "print('Ordinal Encoding')\n",
    "print('number of columns: ', df_train_ordinal.shape[1])\n",
    "print('memory usage: ', df_train_ordinal.memory_usage(deep=True).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-27T23:52:09.079253Z",
     "start_time": "2020-10-27T23:52:08.314572Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 501,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the feature importance just out of curiosity\n",
    "\n",
    "# change default style figure and font size\n",
    "plt.rcParams['figure.figsize'] = 10, 8\n",
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "# like other tree-based models, it can also output the\n",
    "# feature importance plot\n",
    "plot_importance(lgb, importance_type='gain')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For tuning LightGBM's hyperparameter, the documentation page has some pretty good suggestions. [LightGBM Documentation: Parameters Tuning](http://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<font color=black>\n",
    "\n",
    "- [Reference code](http://nbviewer.jupyter.org/github/ethen8181/machine-learning/blob/master/trees/lightgbm.ipynb)\n",
    "- [LightGBM Documentation: Parameters Tuning](http://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html)\n",
    "- [Blog: xgboost’s New Fast Histogram (tree_method = hist)](https://medium.com/data-design/xgboosts-new-fast-histogram-tree-method-hist-a3c08f36234c)\n",
    "- [Blog: Which algorithm takes the crown: Light GBM vs XGBOOST?](https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/)\n",
    "- [Blog: Are categorical variables getting lost in your random forests?](http://roamanalytics.com/2016/10/28/are-categorical-variables-getting-lost-in-your-random-forests/)\n",
    "- [Blog: Lessons Learned From Benchmarking Fast Machine Learning Algorithms](https://blogs.technet.microsoft.com/machinelearning/2017/07/25/lessons-learned-benchmarking-fast-machine-learning-algorithms/)\n",
    "- [Stackoverflow: Why tree-based model do not need one-hot encoding for nominal data?\n",
    "](https://stackoverflow.com/questions/45139834/why-tree-based-model-do-not-need-one-hot-encoding-for-nominal-data)\n",
    "\n",
    "</font>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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     "varRefreshCmd": "print(var_dic_list())"
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   "types_to_exclude": [
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