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caretEnsemble (version 4.0.1)

Ensembles of Caret Models

Description

Functions for creating ensembles of caret models: caretList() and caretStack(). caretList() is a convenience function for fitting multiple caret::train() models to the same dataset. caretStack() will make linear or non-linear combinations of these models, using a caret::train() model as a meta-model.

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Install

install.packages('caretEnsemble')

Monthly Downloads

2,864

Version

4.0.1

License

MIT + file LICENSE

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Repository

https://github.com/zachmayer/caretEnsemble

Homepage

http://zachmayer.github.io/caretEnsemble/

Maintainer

Zachary A Mayer

Last Published

September 12th, 2024

Functions in caretEnsemble (4.0.1)

S3 definition for concatenating caretList

extractCaretTarget

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

data for classification

extractMetric.train

Extract accuracy metrics from a train model

Combine several predictive models via stacking

Construct a default metric

dotplot.caretStack

Comparison dotplot for a caretStack object

Prediction wrapper for train

Create a list of several train models from the caret package

extractCaretTarget.default

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

extractModelName

Extract the method name associated with a single train object

S3 definition for concatenating train objects

Plot a caretList object

plot.caretStack

Plot a caretStack object

as.caretList.default

Convert object to caretList object - For Future Use

Combine several predictive models via weights

Generic function to extract accuracy metrics from various model objects

extractCaretTarget.formula

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

check_caretStack

Check caretStack object

isClassifierAndValidate

Validate a model type

print.summary.caretStack

Print a summary.caretStack object

set_excluded_class_id

Set excluded class id

caretList of regression models

extractMetric.caretList

Extract accuracy metrics from a caretList object

Construct a default train control for use with caretList

extractMetric.caretStack

Extract accuracy metrics from a caretStack object

Generate a specification for fitting a caret model

Greedy optimization for MSE

greedyMSE_caret

caret interface for greedyMSE

caretList of classification models

permutationImportance

Permutation Importance

validateExcludedClass

Validate the excluded class

normalize_to_one

Normalize to One

print.greedyMSE

Print method for greedyMSE

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

varImp.caretStack

Variable importance for caretStack

print.summary.caretList

Print a summary.caretList object

stackedTrainResiduals

Extracted stacked residuals for the autoplot

Wrapper to train caret models

checkCustomModel

Validate a custom caret model info list

predict.caretList

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

predict.caretStack

Make predictions from a caretStack

Index a caretList

dropExcludedClass

Drop Excluded Class

extractBestPreds

Extract the best predictions from a train object

predict.greedyMSE

Predict method for greedyMSE

summary.caretList

Summarize a caretList

print.caretStack

Print a caretStack object

summary.caretStack

Summarize a caretStack object

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

varImp.greedyMSE

variable importance for a greedyMSE model

Calculate a weighted standard deviation

aggregate_mean_or_first

Aggregate mean or first

autoplot.caretStack

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

Convert object to caretList object

as.caretList.list

Convert list to caretList

data for classification

data for regression

data for classification