Decision Trees and Boosting

This lecture covers decision trees as a flexible method for machine learning, illustrating them as a formalization of strategies for decision-making processes. It explains the concept of decision trees, their construction, and their application in classification and regression tasks. The lecture also introduces boosting as a technique to combine multiple predictors sequentially, focusing on AdaBoost and Gradient Boosting algorithms. It discusses the advantages and limitations of decision trees, including their ability to handle mixed data types and the issue of overfitting. The presentation includes examples, algorithms, and practical implementations using Python libraries like scikit-learn.