Decision Trees: Overfitting and Randomization

This lecture covers the concept of decision trees, focusing on the creation of paths in the tree for supervised samples, the issue of overfitting due to labeling every example, and the use of randomization to manage overfitting. The instructor explains the notion of information gain and entropy in decision tree construction, introduces the idea of bootstrap aggregation to reduce variance, and discusses the random vector model for feature selection. The lecture also touches on the application of decision trees in practice, emphasizing the importance of understanding subtleties to avoid overfitting.