Package index • caretEnsemble

All functions

as.caretList(): Convert object to caretList object

as.caretList(<default>): Convert object to caretList object - For Future Use

as.caretList(<list>): Convert list to caretList

autoplot(<caretStack>): Convenience function for more in-depth diagnostic plots of caretStack objects

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

caretStack(): Combine several predictive models via stacking

defaultControl(): Construct a default train control for use with caretList

defaultMetric(): Construct a default metric

dotplot(<caretStack>): Comparison dotplot for a caretStack object

extractMetric(): Generic function to extract accuracy metrics from various model objects

extractMetric(<caretList>): Extract accuracy metrics from a caretList object

extractMetric(<caretStack>): Extract accuracy metrics from a caretStack object

extractMetric(<train>): Extract accuracy metrics from a train model

greedyMSE(): Greedy optimization for MSE

greedyMSE_caret(): caret interface for greedyMSE

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

`[`(<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

varImp(<caretStack>): Variable importance for caretStack

varImp(<greedyMSE>): variable importance for a greedyMSE model

wtd.sd(): Calculate a weighted standard deviation