tree-and-ensemble-models

Build, tune and explain decision trees and tree ensembles: random forest, bagging, AdaBoost, gradient boosting, XGBoost, LightGBM and CatBoost. Use whenever someone asks whether to use a single tree, a random forest or boosting; how to set max_depth, min_samples_leaf, n_estimators, learning rate, max_features or subsample for one of these models; gini or entropy; how to prune a tree; why a tree overfits or a boosted model is unstable; what the out-of-bag score means; how far to trust feature importance, or when to use permutation importance or SHAP instead; how to turn a tree into business rules; or whether a regression tree beats linear regression. Not for designing the validation split or reading bias-variance in general, and not for choosing a classification threshold.