Structured Classifications: Decision Trees and Boosting

This lecture covers decision trees, overfitting elimination, structured models, logical classification learning, and boosting techniques. It explains the process of constructing decision trees, choosing attributes, and formalizing uncertainty through entropy. The instructor also discusses the concept of boosting, which combines weak models to improve accuracy. Various criteria for tree pruning and the use of decision trees for regression are explored. The lecture concludes with examples of boosting algorithms like Adaboost and Martingale boosting, along with real-world applications in predicting electrical network failures.