Decision Trees: Regression and Classification

This lecture covers the fundamentals of decision trees, focusing on regression and classification. It explains the process of tree construction, feature selection, and threshold determination. The instructor discusses the criteria used for decision tree induction, such as Average Squared Loss for regression trees and Gini impurity for classification trees. Additionally, the lecture highlights the characteristics of decision tree induction, including interpretability, minimal data preparation, and automatic feature selection. It also addresses the challenges of overfitting and suboptimal performance, providing insights on pruning techniques to avoid high tree-depth. Various examples are presented to illustrate the concepts.