What is R Caret?
R Caret is an open-source R package that provides a unified interface for machine learning algorithms in R. It stands for Classification and Regression Training. The package contains tools for:
- Splitting data into training and test sets
- Data pre-processing like scaling and transformations
- Feature selection algorithms
- Hyperparameter tuning for model selection
- Flexible metrics for model evaluation
- Estimation of variable importance
By providing a standardized set of functions, R Caret allows data scientists to streamline their machine learning workflows in R without having to rewrite code for data splits, pre-processing, and model tuning for every algorithm. This simplifies everything from prototyping to model building. The package includes over 200 modeling functions covering regression, classification, clustering, time series, and more.
Key capabilities include train/test splits, cross-validation, grid and random searches for hyperparameters, built-in pre-processing methods, and visualizations for calibration curves, variable importance, and much more. R Caret is highly popular among R users due to its flexibility and vast set of modeling capabilities.