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caretEnsemble: (Read the vignette!)

Framework for fitting multiple caret models using the same re-sampling strategy as well as creating ensembles of such models. Use caretList to fit multiple models, and then use caretEnsemble to combine them greedily, or caretStack to combine them using a caret model.

caretEnsemble was inspired by medley, which in turn was inspired by Caruana et. al.'s (2004) paper Ensemble Selection from Libraries of Models.

Install the stable version from CRAN:

install.packages('caretEnsemble')

Install the dev version from github:

devtools::install_github('zachmayer/caretEnsemble')

There are also tagged versions of caretEnsemble on github you can install via devtools. For example, to install the original draft of the API:

devtools::install_github('zachmayer/caretEnsemble@0.0')

Code of Conduct:

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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Install

install.packages('caretEnsemble')

Monthly Downloads

2,864

Version

2.0.3

License

MIT + file LICENSE

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Repository

https://github.com/zachmayer/caretEnsemble

Maintainer

Zachary A Mayer

Last Published

September 20th, 2023

Functions in caretEnsemble (2.0.3)

extractCaretTarget.formula

Extracts the target variable from a set of arguments headed to the caret::train.formula function.

predict.caretList

Create a matrix of predictions for each of the models in a caretList

caretList of classification models

print.caretStack

Print a caretStack object

varImp.caretEnsemble

Calculate the variable importance of variables in a caretEnsemble.

Supplement the data fitted to a caret ensemble model with model fit statistics

Check that the methods supplied by the user are valid caret methods

makePredObsMatrix

Make a prediction matrix from a list of models

extractModelTypes

Extracts the model types from a list of train model

check_caretList_model_types

Checks that caretList models are all of the same type.

residuals.caretEnsemble

Calculate the residuals from a caretEnsemble.

validateBinaryTargetLevel

Validate arguments given as binary target level

validateCustomModel

Validate a custom caret model info list

predict.caretStack

Make predictions from a caretStack

Calculate a weighted standard deviation

Extract accuracy metrics from a model

is.caretEnsemble

Check if an object is a caretEnsemble object

check_caretList_classes

Checks caretList model classes

Check if an object is a caretList object

getBinaryTargetLevel

Return the configured target binary class level

setBinaryTargetLevel

Set the target binary class level

Check if an object is a caretStack object

extractModFrame

Extract a dataframe of all predictors used in a caretEnsemble object.

Index a caretList

Check that the tuning parameters list supplied by the user is valid

Check that the trainControl object supplied by the user is valid and has defined re-sampling indexes.

Calculate the residuals from all component models of a caretEnsemble.

plot.caretStack

Plot a caretStack object

plot.caretEnsemble

Plot Diagnostics for an caretEnsemble Object

Extract the model accuracy metrics of the individual models in an ensemble object.

summary.caretStack

Summarize a caretStack object

summary.caretEnsemble

Summarize the results of caretEnsemble for the user.

extractModelName

Extract the method name associated with a single train object

S3 definition for concatenating caretList

Convenience function for more in-depth diagnostic plots of caretEnsemble objects

Convert object to caretList object

as.caretList.default

Convert object to caretList object - For Future Use

Extract the best predictions from a train object

as.caretList.list

Convert list to caretList

Create a list of several train models from the caret package Build a list of train objects suitable for ensembling using the caretEnsemble function.

S3 definition for concatenating train objects

Generate a specification for fitting a caret model

caretEnsemble: Make ensembles of caret models.

check_bestpreds_obs

Check observeds

check_bestpreds_preds

Check predictions

dotplot.caretStack

Comparison dotplot for a caretStack object

check_bestpreds_resamples

Check resamples

extractBestPreds

Extract the best predictions from a list of train objects

extractCaretTarget

Extracts the target variable from a set of arguments headed to the caret::train function.

extractCaretTarget.default

Extracts the target variable from a set of arguments headed to the caret::train.default function.

Combine several predictive models via stacking

check_bestpreds_indexes

Check row indexes