Decision Trees: Induction and Pruning

This lecture covers the concept of Decision Trees, starting with the basics of tree construction and induction, including the process of selecting attributes and splitting nodes. It then delves into the challenges of handling continuous attributes, discussing binary decision trees and the scalability issues related to continuous attribute splits. The instructor explains the importance of pruning strategies to prevent overfitting and introduces the Minimum Description Length principle. The lecture concludes with a discussion on extracting classification rules from trees, emphasizing the interpretability and automatic feature selection strengths of Decision Trees, while highlighting their sensitivity to data perturbations and tendency to overfit.