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A companion Jupyter notebook to a field guide on tree-based ML models, designed to empirically verify key claims about CART, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, Histogram Gradient Boosting, and Isolation Forest. Using a simulated 12,000-row subscription conversion dataset with intentional noise, the notebook demonstrates: why feature scaling is irrelevant for trees, why extrapolation is structurally impossible, how different metrics produce different model rankings, how bagging reduces variance, how boosting overfits without early stopping, and why impurity-based feature importance (MDI) can reward useless high-cardinality columns while permutation importance on held-out data does not. All experiments run on numpy, pandas, matplotlib, and scikit-learn with no extra installs.
