caretEnsemble (version 1.0.0)

caretEnsemble: Combine several predictive models via weights

Description

Find a good linear combination of several classification or regression models, using either linear regression, elastic net regression, or greedy optimization.

Usage

caretEnsemble(all.models, optFUN = NULL, ...)

Arguments

all.models

an object of class caretList

optFUN

the optimization function to use

...

additional arguments to pass to the optimization function

Value

• a caretEnsemble object

Details

Every model in the "library" must be a separate train object. For example, if you wish to combine a random forests with several different values of mtry, you must build a model for each value of mtry. If you use several values of mtry in one train model, (e.g. tuneGrid = expand.grid(.mtry=2:5)), caret will select the best value of mtry before we get a chance to include it in the ensemble. By default, RMSE is used to ensemble regression models, and AUC is used to ensemble Classification models. This function does not currently support multi-class problems

References

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.60.2859&rep=rep1&type=pdf

Examples

set.seed(42)
models <- caretList(iris[1:50,1:2], iris[1:50,3], methodList=c('glm', 'lm'))
ens <- caretEnsemble(models)
summary(ens)