AdaBoost: Decision Stumps

This lecture covers the AdaBoost algorithm with decision stumps, focusing on a 2-class binary problem. It explains the error and weight update rules of AdaBoost, the selection of decision stumps, and why two stumps are insufficient for perfect classification. The solutions to the exercises involve selecting the best decision stumps based on weights and misclassified points. Additionally, it discusses the linear combination of weak learners in AdaBoost and the need for a bias term to achieve perfect classification. The lecture also explores the concept of weak classifiers and the importance of selecting the proper model for classification problems.