Cross-validate a bagged tree model in caret

Use caret::train() with the "treebag" method to train a model and evaluate the model using cross-validated AUC. The caret package allows the user to easily cross-validate any model across any relevant performance metric. In this case, we will use 5-fold cross validation and evaluate cross-validated AUC (Area Under the ROC Curve).

The credit_train dataset is in your workspace. You will use this data frame as the training data.

First specify a ctrl object, which is created using the caret::trainControl() function.
In the trainControl() function, you can specify many things. We will set: method = "cv", number = 5 for 5-fold cross-validation. Also, two options that are required if you want to use AUC as the metric: classProbs = TRUE and summaryFunction = twoClassSummary.

Exercise

Cross-validate a bagged tree model in caret

Instructions