Classical ML
Trees & Ensembles
From a single greedy tree to the ensembles that win tabular ML — and why averaging and boosting work
Decision trees and random forests
coreGreedy splits scored by Gini or entropy build an interpretable but high-variance learner; bagging plus feature subsampling turns a crowd of them into a forest whose variance shrinks like
12 mintreesensemblesGradient boosting
advancedFit a small tree to the current errors, add a shrunken copy, repeat — gradient descent in function space. The bias-killer to bagging's variance-killer, and the reason a learning rate exists in a tree model
12 minensemblesboosting