The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - Provides a comprehensive theoretical and practical treatment of decision trees, ensemble methods like bagging, and random forests, including their statistical properties and algorithms.
Random Forests, Leo Breiman, 2001Machine Learning, Vol. 45DOI: 10.1023/A:1010933404324 - The original academic paper introducing the Random Forest algorithm, detailing its construction and theoretical underpinnings.
1.10. Decision Trees (scikit-learn User Guide), scikit-learn developers, 2024 - Official documentation for Decision Trees in scikit-learn, covering usage, parameters, and algorithms like CART.
1.11. Ensemble methods (scikit-learn User Guide), scikit-learn developers, 2024 - Official documentation for ensemble methods in scikit-learn, specifically detailing Random Forests, their parameters, and their advantages.