caret

Optimal performance with Random Forests: does feature selection beat tuning?

This blog post demonstrates that the presence of irrelevant variables can reduce the performance of the Random Forest algorithm (as implemented in R by ranger()). The solution is either to tune one of the algorithm's parameters, OR to remove irrelevant features using a procedure called Recursive Feature Elimination (RFE).

The validation set approach in caret

In this blog post, we explore how to implement the validation set approach in caret. This is the most basic form of the train/test machine learning concept.