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
Practice
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