Lecture
This lecture covers decision trees and random forests, focusing on their application in regression and classification tasks. The instructor explains the structure of decision trees, including nodes, branches, and leaves, and how they can be interpretable when not overly complex. The challenges of finding optimal decision trees are discussed, along with greedy algorithms that sequentially add nodes based on the best-performing features and thresholds. The lecture also addresses the concepts of train and test error in machine learning, emphasizing the importance of ensemble methods to enhance performance. Specifically, bootstrapping is introduced as a technique used in random forests to improve model accuracy. The instructor provides examples and visualizations to illustrate these concepts, including a case study on penguin data to demonstrate classification trees. The lecture concludes with a discussion on the trade-offs involved in model complexity and generalization, highlighting the significance of minimizing test error for effective machine learning models.