caretEnsemble4.0.1 package

Ensembles of Caret Models

aggregate_mean_or_first

Aggregate mean or first

as.caretList.default

Convert object to caretList object - For Future Use

as.caretList.list

Convert list to caretList

as.caretList

Convert object to caretList object

autoplot.caretStack

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

c.caretList

S3 definition for concatenating caretList

c.train

S3 definition for concatenating train objects

caretEnsemble

Combine several predictive models via weights

caretList

Create a list of several train models from the caret package

caretModelSpec

Generate a specification for fitting a caret model

caretPredict

Prediction wrapper for train

caretStack

Combine several predictive models via stacking

caretTrain

Wrapper to train caret models

check_caretStack

Check caretStack object

checkCustomModel

Validate a custom caret model info list

defaultControl

Construct a default train control for use with caretList

defaultMetric

Construct a default metric

dotplot.caretStack

Comparison dotplot for a caretStack object

dropExcludedClass

Drop Excluded Class

extractBestPreds

Extract the best predictions from a train object

extractCaretTarget.default

Extracts the target variable from a set of arguments headed to the car...

extractCaretTarget.formula

Extracts the target variable from a set of arguments headed to the car...

extractCaretTarget

Extracts the target variable from a set of arguments headed to the car...

extractMetric.caretList

Extract accuracy metrics from a caretList object

extractMetric.caretStack

Extract accuracy metrics from a caretStack object

extractMetric

Generic function to extract accuracy metrics from various model object...

extractMetric.train

Extract accuracy metrics from a train model

extractModelName

Extract the method name associated with a single train object

greedyMSE_caret

caret interface for greedyMSE

greedyMSE

Greedy optimization for MSE

isClassifier

Is Classifier

isClassifierAndValidate

Validate a model type

mae

Compute MAE

methodCheck

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

normalize_to_one

Normalize to One

permutationImportance

Permutation Importance

plot.caretList

Plot a caretList object

plot.caretStack

Plot a caretStack object

predict.caretList

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

predict.caretStack

Make predictions from a caretStack

predict.greedyMSE

Predict method for greedyMSE

print.caretStack

Print a caretStack object

print.greedyMSE

Print method for greedyMSE

print.summary.caretList

Print a summary.caretList object

print.summary.caretStack

Print a summary.caretStack object

set_excluded_class_id

Set excluded class id

shuffled_mae

Shuffled MAE

stackedTrainResiduals

Extracted stacked residuals for the autoplot

sub-.caretList

Index a caretList

summary.caretList

Summarize a caretList

summary.caretStack

Summarize a caretStack object

tuneCheck

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

validateExcludedClass

Validate the excluded class

varImp.caretStack

Variable importance for caretStack

varImp.greedyMSE

variable importance for a greedyMSE model

wtd.sd

Calculate a weighted standard deviation

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.

  • Maintainer: Zachary A. Deane-Mayer
  • License: MIT + file LICENSE
  • Last published: 2024-09-12