Bagging and Random Forests

This lecture covers the concepts of ensembling, bagging, and random forests, focusing on variance reduction, OOB samples, variable importance, and the algorithm details. It explains how bagging works for the square loss and its limitations with different predictors. The instructor also discusses the key idea behind random forests, which aim to decrease correlations further by randomizing the tree construction. Additionally, the lecture explores the concept of out-of-bag cross-validation and various measures of variable importance in random forests, such as the Gini VI score and permutation VI score.