Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.
Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
