Decision Trees and Ensemble Methods
Dive into the world of decision trees and ensemble methods in this comprehensive 2.5-hour course. Explore fundamental concepts such as impurity functions, CART algorithm, and basic properties of decision trees. Learn about regularization techniques and how to implement decision trees using Scikit-learn. Delve into advanced topics like bias-variance trade-off, ExtraTrees algorithm, bootstrap, bagging, and random forests. Discover the power of ensemble methods, including proximities, feature importance, and various boosting techniques such as gradient boosting, XGBoost, LightGBM, and CatBoost. Gain practical experience through examples and notebook exercises, equipping you with the skills to apply these powerful machine learning techniques to real-world problems.