Machine Learning with caret in R

Description

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).

Syllabus :

• 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