Tutorial
Jun 26, 202610 min read
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Bagging and Boosting: Ensemble Intuition
No single model is perfect. A decision tree with the wrong depth misses the boundary. A linear model can't bend around nonlinear data. Ensemble methods don't so…
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Ensemble
Tutorial
Jun 26, 20269 min read
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Random Forest: Algorithm and Regression
Bagging trains independent trees on bootstrap samples. Random Forest adds one more source of randomness: at each split, only a random subset of features is cons…
Tutorial
Jun 26, 20268 min read
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Random Forest: Feature Importance and Feature Engineering
A trained Random Forest carries embedded feature importance scores — a side effect of building trees. This post covers two ways to extract them (impurity-based…
Tutorial
Jun 26, 202611 min read
0
Random Forest: Forest Cover Type Project
This is the Random Forest capstone: a full pipeline from raw data to deployment-ready model on Forest Cover Type — 580k samples, 54 features, 7 classes. It demo…
Tutorial
Jun 26, 20269 min read
0
AdaBoost: Algorithm Intuition
AdaBoost (Adaptive Boosting) builds a strong classifier from many weak ones — each a decision stump (depth-1 tree). The key: after each round, samples that were…