Decision Trees and Ensemble Learning

About this session

Explore the fundamentals of Decision Trees and Ensemble Learning, including Random Forest and XGBoost. This session explains how decision trees make predictions using concepts such as entropy, information gain, and recursive splitting, while examining the bias-variance tradeoff and techniques to prevent overfitting. Learn how ensemble methods like bagging and boosting improve predictive performance by combining multiple models, and understand the differences between Random Forest and XGBoost in terms of accuracy, efficiency, and use cases. The session also covers hyperparameter tuning, feature importance, model evaluation metrics, and practical considerations for selecting tree-based algorithms to solve real-world classification and regression problems across a variety of machine learning applications.