In this course, you will :
- you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
- you'll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
- The train() function will be used to fine-tune model parameters using cross-validation and grid search.
- Train() to preprocess data before fitting models to improve your ability to make accurate predictions.
- Discover how to use resamples() to compare multiple models and choose (or ensemble) the best one (s).
- Regression models: fitting them and evaluating their performance
- Classification models: fitting them and evaluating their performance
- Tuning model parameters to improve performance
- Preprocessing your data
- Selecting models: a case study in churn prediction