Machine Learning with caret in R

In the first chapter of this course, you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).

In this chapter, you'll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).

In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.

In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.

In the final chapter of this course, you'll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).