Lecture
This lecture covers the principles of decision trees, including their construction, attribute selection, and pruning. It explains how decision trees are used for classification tasks, the importance of entropy in attribute selection, and the process of tree induction. The instructor also discusses the bias-variance tradeoff in decision tree models, comparing random forests and boosted trees. Additionally, it explores the concept of ensemble methods in machine learning, such as bagging and stacking. The lecture concludes with insights on model transparency and the challenges of interpreting complex models.