Lecture
This lecture delves into the concept of overfitting in decision trees, explaining how the complexity of models can lead to poor generalization. The instructor discusses the impact of overfitting on prediction error and introduces the idea of random forests as a solution to manage overfitting. The lecture covers bootstrapping, bagging, and the random vector model as methods to reduce variance and improve the performance of decision trees. The instructor also touches upon the importance of bias reduction through boosting, providing a comprehensive overview of techniques to enhance the accuracy and generalizability of machine learning models.