Decision Trees

Interpretable models that partition feature space with axis-aligned splits. The foundation for ensemble methods like Random Forests and Gradient Boosting.

Learning Objectives

• →Understand Gini impurity and information gain
• →Trace a decision path from root to leaf
• →Explain how depth controls overfitting
• →Describe feature importance in tree models

Coding Problems (2)