Ensembling models
That concludes the course! As a teaser for a future course on making ensembles of caret
models, I'll show you how to fit a stacked ensemble of models using the caretEnsemble
package.
caretEnsemble
provides the caretList()
function for creating multiple caret
models at once on the same dataset, using the same resampling folds. You can also create your own lists of caret
models.
In this exercise, I've made a caretList
for you, containing the glmnet
and ranger
models you fit on the churn dataset. Use the caretStack()
function to make a stack of caret
models, with the two sub-models (glmnet
and ranger
) feeding into another (hopefully more accurate!) caret
model.
This exercise is part of the course
Machine Learning with caret in R
Exercise instructions
- Call the
caretStack()
function with two arguments,model_list
andmethod = "glm"
, to ensemble the two models using a logistic regression. Store the result asstack
. - Summarize the resulting model with the
summary()
function.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create ensemble model: stack
stack <-
# Look at summary