This cheat sheet provides a visual overview of common machine learning algorithms available in R, covering both supervised and unsupervised methods with their R package functions. It also illustrates meta-algorithms such as boosting and bagging, a standard modelling workflow for training and validation, and a time-series perspective on model deployment.
What’s covered#
- Supervised & unsupervised learning – algorithm descriptions and R functions for classification, regression, clustering, and dimensionality reduction
- Meta-algorithms – boosting, bagging, stacking, and random forests
- Time series & model validation – cross-validation and model selection strategies
- Standard modelling workflow – training, validation, and production stages with simple and meta models
- Time series view – train/validate/production split over time with simple and meta model aggregation