Decision Trees and Boosting

This lecture covers decision trees in machine learning, explaining how they formalize strategies for decision-making processes. It delves into the recursive partitioning of feature space, the process of asking questions to make predictions, and the impurity criteria used for splitting nodes. The instructor also discusses the flexibility and limitations of decision trees, including their susceptibility to noise in data. Additionally, the lecture introduces boosting as a method to combine multiple predictors sequentially, focusing on the Adaboost algorithm and its application in gradient boosting. Practical examples and algorithms such as CART and random forests are also explored.