Compare a single classification tree in R with 500 bagged trees on out-of-bag and test errors, cumulative errors and variable importance measures.

Evaluate a single regression tree in R against 2000 bagged trees, comparing out-of-bag and test errors, cumulative errors and variable importance.

Part IV of tree based methods in R series. 3 R packages for classification analysis are compared - rpart, caret and mlr packages.

Fit a regression tree on the Carseats data in R, comparing the pruning parameter caret selects against the 1-SE rule that the rpart package recommends.

Cost-sensitive classification with rpart and caret in R, treating a missed High class as twice as expensive by altering the priors and the loss matrix.

Fit a CART classification model on the ISLR Carseats data with the rpart package in R, and tune the pruning parameter with the caret package.