diff --git a/NAMESPACE b/NAMESPACE
index de50241d..86c5b495 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -23,7 +23,6 @@ export(createDefaultExecuteSettings)
 export(createDefaultSplitSetting)
 export(createExecuteSettings)
 export(createExistingSplitSettings)
-export(createFeatureEngineeringMapColumnsSettings)
 export(createFeatureEngineeringSettings)
 export(createGlmModel)
 export(createLearningCurve)
@@ -34,7 +33,7 @@ export(createPreprocessSettings)
 export(createRandomForestFeatureSelection)
 export(createRestrictPlpDataSettings)
 export(createSampleSettings)
-export(createSciKitLearnModel)
+export(createSklearnModel)
 export(createSplineSettings)
 export(createStratifiedImputationSettings)
 export(createStudyPopulation)
diff --git a/R/ExistingPython.R b/R/ExistingPython.R
deleted file mode 100644
index f78dc279..00000000
--- a/R/ExistingPython.R
+++ /dev/null
@@ -1,200 +0,0 @@
-# @file ExistingPython.R
-#
-# Copyright 2025 Observational Health Data Sciences and Informatics
-#
-# This file is part of PatientLevelPrediction
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-#' Plug an existing scikit learn python model into the
-#' PLP framework
-#'
-#' @details
-#' This function lets users add an existing scikit learn that is saved as model.pkl
-#' into PLP format.  covariateMap is a mapping between standard covariateIds and the model column names
-#' and order are required in addition to pythonModelLocation, the location of the model that must be saved
-#' as `model.pkl`.  The user also needs to specify the covariate settings and population settings as these
-#' are used to determine the standard PLP model design.
-#'
-#' @param modelLocation  The location of the folder that contains the model as model.pkl
-#' @param covariateMap  A data.frame with the columns: columnId specifying the column order for the
-#' covariate, covariateId the covariate ID from FeatureExtraction and modelCovariateIdName which is the
-#' column name used when fitting the model.  For example, if you had a column called 'age' in your model and this was the 3rd
-#' column when fitting the model, then the values for columnId would be 3, covariateId would be 1002 (the covariateId for age in years) and
-#' modelCovariateIdName would be 'age'.
-#' @param covariateSettings  The settings for the standardized covariates
-#' @param populationSettings  The settings for the population, this includes the time-at-risk settings and
-#' and inclusion criteria.
-#' @param isPickle If the model is saved as a pickle set this to T if it is a json set this to F
-#'
-#' @return
-#' An object of class plpModel, this is a list that contains: model (the location of the model.pkl),
-#' preprocessing (settings for mapping the covariateIds to the model column mames), modelDesign (specification
-#' of the model design), trainDetails (information about the model fitting) and covariateImportance.  You can use the output
-#' as an input in PatientLevelPrediction::predictPlp to apply the model and calculate the risk for patients.
-#'
-#' @export
-createSciKitLearnModel <- function(
-    modelLocation = "/model", # model needs to be saved here as "model.pkl"
-    covariateMap = data.frame(
-      columnId = 1:2,
-      covariateId = c(1, 2),
-      modelCovariateIdName = c("pred_1", "pred_2")
-    ),
-    covariateSettings, # specify the covariates
-    populationSettings, # specify time at risk used to develop model
-    isPickle = TRUE) {
-  checkSklearn()
-  checkFileExists(modelLocation)
-  checkIsClass(covariateMap, "data.frame")
-  checkIsClass(covariateSettings, "covariateSettings")
-  checkIsClass(populationSettings, "populationSettings")
-  checkBoolean(isPickle)
-  checkDataframe(covariateMap, c("columnId", "covariateId", "modelCovariateIdName"),
-    columnTypes = list(c("numeric", "integer"), "numeric", "character")
-  )
-  existingModel <- list(model = "existingPython")
-  class(existingModel) <- "modelSettings"
-
-  plpModel <- list(
-    # use plpModel$preprocessing$featureEngineering to rename columns
-    # set plpModel$preprocessing$tidyCovariates to NULL
-    preprocessing = list(
-      featureEngineering = list(
-        funct = "mapColumns",
-        settings = list(
-          featureEngineeringSettings = createFeatureEngineeringMapColumnsSettings(
-            columnMap = covariateMap
-          )
-        )
-      ),
-      tidyCovariates = NULL,
-      requireDenseMatrix = FALSE
-    ),
-    covariateImportance = data.frame(
-      columnId = covariateMap$columnId,
-      covariateId = covariateMap$modelCovariateIdName,
-      included = TRUE
-    ),
-    modelDesign = PatientLevelPrediction::createModelDesign(
-      targetId = 1,
-      outcomeId = 2,
-      restrictPlpDataSettings = PatientLevelPrediction::createRestrictPlpDataSettings(),
-      covariateSettings = covariateSettings,
-      populationSettings = populationSettings,
-      sampleSettings = PatientLevelPrediction::createSampleSettings(),
-      featureEngineeringSettings = createFeatureEngineeringMapColumnsSettings(
-        columnMap = covariateMap
-      ),
-      preprocessSettings = PatientLevelPrediction::createPreprocessSettings(
-        minFraction = 0,
-        normalize = FALSE,
-        removeRedundancy = FALSE
-      ),
-      modelSettings = existingModel,
-      splitSettings = PatientLevelPrediction::createDefaultSplitSetting()
-    ),
-    model = modelLocation,
-    trainDetails = list(
-      analysisId = "exisitingPython",
-      developmentDatabase = "nonOMOP",
-      developmentDatabaseId = "nonOMOP",
-      trainingTime = -1,
-      modelName = "existing"
-    )
-  )
-
-  attr(plpModel, "modelType") <- "binary"
-  attr(plpModel, "saveType") <- "file"
-  attr(plpModel, "predictionFunction") <- "predictPythonSklearn"
-  attr(plpModel, "saveToJson") <- !isPickle
-  class(plpModel) <- "plpModel"
-  return(plpModel)
-}
-
-
-#' Create settings that enable you to convert from standard covariateIds to
-#' model covariate names - this is useful when implementing a model developed
-#' outside of the OHDSI tools.
-#'
-#' @details
-#' This function creates settings that let you rename the covariates in the plpData object
-#'
-#' @param columnMap A data.frame containing the columns: covariateId the covariate ID from FeatureExtraction and
-#' modelCovariateIdName which is the column name used when fitting the model.
-#'
-#' @return
-#' An object of class \code{featureEngineeringSettings} that will convert column names
-#'
-#' @export
-createFeatureEngineeringMapColumnsSettings <- function(
-    columnMap) {
-  featureEngineeringSettings <- list(
-    columnMap = columnMap
-  )
-
-  attr(featureEngineeringSettings, "fun") <- "mapColumns"
-  class(featureEngineeringSettings) <- "featureEngineeringSettings"
-  return(featureEngineeringSettings)
-}
-
-mapColumns <- function(
-    trainData,
-    featureEngineeringSettings) {
-  ParallelLogger::logInfo("Changing column names")
-
-  # map the columns - swap the covariateId with the modelCovariateIdName
-  trainData$covariateData$columnMap <- featureEngineeringSettings$columnMap %>%
-    dplyr::select("covariateId", "modelCovariateIdName")
-  trainData$covariateData$covariates <- trainData$covariateData$covariates %>%
-    dplyr::rename(newId = "rowId") %>% # duckdb issue with implicit rowid
-    dplyr::collapse() %>% 
-    dplyr::inner_join(
-      trainData$covariateData$columnMap,
-      by = "covariateId"
-    ) %>%
-    dplyr::select("newId", "modelCovariateIdName", "covariateValue") %>%
-    dplyr::rename(
-      rowId = "newId", # duckdb issue with implicit rowid
-      covariateId = "modelCovariateIdName"
-    )
-
-  trainData$covariateData$covariateRef <- dplyr::inner_join(
-    trainData$covariateData$covariateRef,
-    trainData$covariateData$columnMap,
-    by = "covariateId"
-  ) %>%
-    dplyr::select(-"covariateId") %>%
-    dplyr::rename(
-      covariateId = "modelCovariateIdName"
-    )
-
-  # remove the columnMap
-  trainData$covariateData$columnMap <- NULL
-
-  # add attribute for FE
-  featureEngineering <- list(
-    funct = "mapColumns",
-    settings = list(
-      featureEngineeringSettings = featureEngineeringSettings
-    )
-  )
-
-  attr(trainData, "metaData")$featureEngineering <- listAppend(
-    attr(trainData, "metaData")$featureEngineering,
-    featureEngineering
-  )
-
-  return(trainData)
-}
diff --git a/R/ExistingSklearn.R b/R/ExistingSklearn.R
new file mode 100644
index 00000000..5e02b778
--- /dev/null
+++ b/R/ExistingSklearn.R
@@ -0,0 +1,119 @@
+# @file ExistingSklearn.R
+#
+# Copyright 2025 Observational Health Data Sciences and Informatics
+#
+# This file is part of PatientLevelPrediction
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+#' Plug an existing scikit learn python model into the
+#' PLP framework
+#'
+#' @details
+#' This function lets users add an existing scikit learn model that is saved as 
+#' model.pkl into PLP format.  covariateMap is a mapping between standard 
+#' covariateIds and the model columns. The user also needs to specify the 
+#' covariate settings and population settings as these are used to determine 
+#' the standard PLP model design.
+#'
+#' @param modelLocation  The location of the folder that contains the model as
+#' model.pkl
+#' @param covariateMap  A data.frame with the columns: columnId and covariateId. 
+#' `covariateId` from FeatureExtraction is the standard OHDSI covariateId.
+#' `columnId` is the column location the model expects that covariate to be in.
+#' For example, if you had a column called 'age' in your model and this was the 
+#' 3rd column when fitting the model, then the values for columnId would be 3, 
+#' covariateId would be 1002 (the covariateId for age in years) and
+#' @param covariateSettings  The settings for the standardized covariates
+#' @param populationSettings  The settings for the population, this includes the 
+#' time-at-risk settings and inclusion criteria.
+#' @param isPickle If the model should be saved as a pickle set this to TRUE if
+#' it should be saved as json set this to FALSE.
+#'
+#' @return
+#' An object of class plpModel, this is a list that contains: 
+#'        model (the location of the model.pkl),
+#'        preprocessing (settings for mapping the covariateIds to the model 
+#'                       column mames), 
+#'        modelDesign (specification of the model design), 
+#'        trainDetails (information about the model fitting) and 
+#'        covariateImportance.  
+#' 
+#' You can use the output as an input in PatientLevelPrediction::predictPlp to 
+#' apply the model and calculate the risk for patients.
+#'
+#' @export
+createSklearnModel <- function(
+    modelLocation = "/model", # model needs to be saved here as "model.pkl"
+    covariateMap = data.frame(
+      columnId = 1:2,
+      covariateId = c(1, 2),
+    ),
+    covariateSettings, # specify the covariates
+    populationSettings, # specify time at risk used to develop model
+    isPickle = TRUE) {
+  checkSklearn()
+  checkFileExists(modelLocation)
+  checkIsClass(covariateMap, "data.frame")
+  checkIsClass(covariateSettings, "covariateSettings")
+  checkIsClass(populationSettings, "populationSettings")
+  checkBoolean(isPickle)
+  checkDataframe(covariateMap, c("columnId", "covariateId"),
+    columnTypes = list(c("numeric", "integer"), "numeric")
+  )
+  existingModel <- list(model = "existingSklearn")
+  class(existingModel) <- "modelSettings"
+
+  plpModel <- list(
+    preprocessing = list(
+      tidyCovariates = NULL,
+      requireDenseMatrix = FALSE
+    ),
+    covariateImportance = data.frame(
+      columnId = covariateMap$columnId,
+      covariateId = covariateMap$covariateId,
+      included = TRUE
+    ),
+    modelDesign = PatientLevelPrediction::createModelDesign(
+      targetId = 1,
+      outcomeId = 2,
+      restrictPlpDataSettings = PatientLevelPrediction::createRestrictPlpDataSettings(),
+      covariateSettings = covariateSettings,
+      populationSettings = populationSettings,
+      sampleSettings = PatientLevelPrediction::createSampleSettings(),
+      preprocessSettings = PatientLevelPrediction::createPreprocessSettings(
+        minFraction = 0,
+        normalize = FALSE,
+        removeRedundancy = FALSE
+      ),
+      modelSettings = existingModel,
+      splitSettings = PatientLevelPrediction::createDefaultSplitSetting()
+    ),
+    model = modelLocation,
+    trainDetails = list(
+      analysisId = "existingSklearn",
+      developmentDatabase = "unknown",
+      developmentDatabaseId = "unknown",
+      trainingTime = -1,
+      modelName = "existingSklearn"
+    )
+  )
+
+  attr(plpModel, "modelType") <- "binary"
+  attr(plpModel, "saveType") <- "file"
+  attr(plpModel, "predictionFunction") <- "predictPythonSklearn"
+  attr(plpModel, "saveToJson") <- !isPickle
+  class(plpModel) <- "plpModel"
+  return(plpModel)
+}
diff --git a/man/createFeatureEngineeringMapColumnsSettings.Rd b/man/createFeatureEngineeringMapColumnsSettings.Rd
deleted file mode 100644
index e6494813..00000000
--- a/man/createFeatureEngineeringMapColumnsSettings.Rd
+++ /dev/null
@@ -1,25 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/ExistingPython.R
-\name{createFeatureEngineeringMapColumnsSettings}
-\alias{createFeatureEngineeringMapColumnsSettings}
-\title{Create settings that enable you to convert from standard covariateIds to
-model covariate names - this is useful when implementing a model developed
-outside of the OHDSI tools.}
-\usage{
-createFeatureEngineeringMapColumnsSettings(columnMap)
-}
-\arguments{
-\item{columnMap}{A data.frame containing the columns: covariateId the covariate ID from FeatureExtraction and
-modelCovariateIdName which is the column name used when fitting the model.}
-}
-\value{
-An object of class \code{featureEngineeringSettings} that will convert column names
-}
-\description{
-Create settings that enable you to convert from standard covariateIds to
-model covariate names - this is useful when implementing a model developed
-outside of the OHDSI tools.
-}
-\details{
-This function creates settings that let you rename the covariates in the plpData object
-}
diff --git a/man/createSciKitLearnModel.Rd b/man/createSciKitLearnModel.Rd
deleted file mode 100644
index 92819d19..00000000
--- a/man/createSciKitLearnModel.Rd
+++ /dev/null
@@ -1,49 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/ExistingPython.R
-\name{createSciKitLearnModel}
-\alias{createSciKitLearnModel}
-\title{Plug an existing scikit learn python model into the
-PLP framework}
-\usage{
-createSciKitLearnModel(
-  modelLocation = "/model",
-  covariateMap = data.frame(columnId = 1:2, covariateId = c(1, 2), modelCovariateIdName =
-    c("pred_1", "pred_2")),
-  covariateSettings,
-  populationSettings,
-  isPickle = TRUE
-)
-}
-\arguments{
-\item{modelLocation}{The location of the folder that contains the model as model.pkl}
-
-\item{covariateMap}{A data.frame with the columns: columnId specifying the column order for the
-covariate, covariateId the covariate ID from FeatureExtraction and modelCovariateIdName which is the
-column name used when fitting the model.  For example, if you had a column called 'age' in your model and this was the 3rd
-column when fitting the model, then the values for columnId would be 3, covariateId would be 1002 (the covariateId for age in years) and
-modelCovariateIdName would be 'age'.}
-
-\item{covariateSettings}{The settings for the standardized covariates}
-
-\item{populationSettings}{The settings for the population, this includes the time-at-risk settings and
-and inclusion criteria.}
-
-\item{isPickle}{If the model is saved as a pickle set this to T if it is a json set this to F}
-}
-\value{
-An object of class plpModel, this is a list that contains: model (the location of the model.pkl),
-preprocessing (settings for mapping the covariateIds to the model column mames), modelDesign (specification
-of the model design), trainDetails (information about the model fitting) and covariateImportance.  You can use the output
-as an input in PatientLevelPrediction::predictPlp to apply the model and calculate the risk for patients.
-}
-\description{
-Plug an existing scikit learn python model into the
-PLP framework
-}
-\details{
-This function lets users add an existing scikit learn that is saved as model.pkl
-into PLP format.  covariateMap is a mapping between standard covariateIds and the model column names
-and order are required in addition to pythonModelLocation, the location of the model that must be saved
-as `model.pkl`.  The user also needs to specify the covariate settings and population settings as these
-are used to determine the standard PLP model design.
-}
diff --git a/man/createSklearnModel.Rd b/man/createSklearnModel.Rd
new file mode 100644
index 00000000..f66e8cca
--- /dev/null
+++ b/man/createSklearnModel.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ExistingSklearn.R
+\name{createSklearnModel}
+\alias{createSklearnModel}
+\title{Plug an existing scikit learn python model into the
+PLP framework}
+\usage{
+createSklearnModel(
+  modelLocation = "/model",
+  covariateMap = data.frame(columnId = 1:2, covariateId = c(1, 2), ),
+  covariateSettings,
+  populationSettings,
+  isPickle = TRUE
+)
+}
+\arguments{
+\item{modelLocation}{The location of the folder that contains the model as
+model.pkl}
+
+\item{covariateMap}{A data.frame with the columns: columnId and covariateId. 
+`covariateId` from FeatureExtraction is the standard OHDSI covariateId.
+`columnId` is the column location the model expects that covariate to be in.
+For example, if you had a column called 'age' in your model and this was the 
+3rd column when fitting the model, then the values for columnId would be 3, 
+covariateId would be 1002 (the covariateId for age in years) and}
+
+\item{covariateSettings}{The settings for the standardized covariates}
+
+\item{populationSettings}{The settings for the population, this includes the 
+time-at-risk settings and inclusion criteria.}
+
+\item{isPickle}{If the model should be saved as a pickle set this to TRUE if
+it should be saved as json set this to FALSE.}
+}
+\value{
+An object of class plpModel, this is a list that contains: 
+       model (the location of the model.pkl),
+       preprocessing (settings for mapping the covariateIds to the model 
+                      column mames), 
+       modelDesign (specification of the model design), 
+       trainDetails (information about the model fitting) and 
+       covariateImportance.  
+
+You can use the output as an input in PatientLevelPrediction::predictPlp to 
+apply the model and calculate the risk for patients.
+}
+\description{
+Plug an existing scikit learn python model into the
+PLP framework
+}
+\details{
+This function lets users add an existing scikit learn model that is saved as 
+model.pkl into PLP format.  covariateMap is a mapping between standard 
+covariateIds and the model columns. The user also needs to specify the 
+covariate settings and population settings as these are used to determine 
+the standard PLP model design.
+}
diff --git a/tests/testthat/test-existingModel.R b/tests/testthat/test-existingModel.R
index 11a22f5d..eb59ddb1 100644
--- a/tests/testthat/test-existingModel.R
+++ b/tests/testthat/test-existingModel.R
@@ -1,47 +1,46 @@
 test_that("Create existing sklearn works", {
-  expect_error(createSciKitLearnModel("existing"))
+  expect_error(createSklearnModel("existing"))
   # create a file model.pkl for testing
   file.create("model.pkl")
   covariateSettings <-
     FeatureExtraction::createCovariateSettings(useDemographicsAge = TRUE)
   populationSettings <- createStudyPopulationSettings()
   # dataframe wrong type
-  expect_error(createSciKitLearnModel(
+  expect_error(createSklearnModel(
     modelLocation = "model.pkl",
     covariateMap = list(
       columnId = "columnId",
-      modelCovariateIdName = "modelCovariateIdName"
+      covariateId = c(1)
     ),
     covariateSettings = covariateSettings,
     populationSettings = populationSettings
   ))
   # dataframe wrong column names
-  expect_error(createSciKitLearnModel(
+  expect_error(createSklearnModel(
     modelLocation = "model.pkl",
     covariateMap = data.frame(
-      columnId = "columnId",
-      modelCovariateIdName = "modelCovariateIdName"
+      columnId = c(1),
+      notCovariateId = c(1002),
     ),
     covariateSettings = covariateSettings,
     populationSettings = populationSettings
   ))
   # dataframe wrong column types
-  expect_error(createSciKitLearnModel(
+  expect_error(createSklearnModel(
     modelLocation = "model.pkl",
     covariateMap = data.frame(
       columnId = 1,
-      modelCovariateIdName = 2
+      covariateId = "2"
     ),
     covariateSettings = covariateSettings,
     populationSettings = populationSettings
   ))
 
-  model <- createSciKitLearnModel(
+  model <- createSklearnModel(
     modelLocation = "model.pkl",
     covariateMap = data.frame(
       columnId = c(1, 2),
-      covariateId = c(1002, 1003),
-      modelCovariateIdName = c("feature1", "feature2")
+      covariateId = c(1002, 1003)
     ),
     covariateSettings = covariateSettings,
     populationSettings = populationSettings
@@ -51,9 +50,6 @@ test_that("Create existing sklearn works", {
   expect_equal(attr(model, "predictionFunction"), "predictPythonSklearn")
   expect_equal(attr(model, "saveToJson"), FALSE)
   expect_equal(class(model), "plpModel")
-  expect_equal(model$preprocessing$featureEngineering$funct, "mapColumns")
-  expect_equal(model$preprocessing$featureEngineering$settings$featureEngineeringSettings$columnMap$columnId, c(1, 2))
-  expect_equal(model$preprocessing$featureEngineering$settings$featureEngineeringSettings$columnMap$modelCovariateIdName, c("feature1", "feature2"))
   unlink("model.pkl")
 })
 
@@ -87,11 +83,10 @@ test_that("existing sklearn model works", {
   joblib <- reticulate::import("joblib")
   joblib$dump(model, file.path(plpModel$model, "model.pkl"))
 
-  # extract covariatMap from plpModel
+  # extract covariateMap from plpModel
   covariateMap <- plpModel$covariateImportance %>% dplyr::select(columnId, covariateId)
-  covariateMap$modelCovariateIdName <- as.character(covariateMap$covariateId)
   
-  existingModel <- createSciKitLearnModel(
+  existingModel <- createSklearnModel(
     modelLocation = file.path(plpModel$model),
     covariateMap = covariateMap,
     covariateSettings = plpModel$modelDesign$covariateSettings,