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Exercise

Random forest with custom trainControl

Another one of my favorite models is the random forest, which combines an ensemble of non-linear decision trees into a highly flexible (and usually quite accurate) model.

Rather than using the classic randomForest package, you'll be using the ranger package, which is a re-implementation of randomForest that produces almost the exact same results, but is faster, more stable, and uses less memory. I highly recommend it as a starting point for random forest modeling in R.

Instructions

100 XP

churn_x and churn_y are loaded in your workspace.

  • Fit a random forest model to the churn dataset. Be sure to use myControl as the trainControl like you've done before and implement the "ranger" method.