algorithm:random-forest
Random Forest
Random Forest in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Trees, forests & gradient boosting
Trees as learned decision rules
A tree learns readable local rules; constraints keep those rules from becoming a lookup table.
Trees, forests & gradient boostingRandom forests and diversity
A forest is strong when its trees are individually useful and collectively disagree in the right ways.
Trees, forests & gradient boostingGradient boosting as error correction
Boosting learns by repeatedly asking: what structured error remains?
Trees, forests & gradient boostingXGBoost: regularized boosting at scale
XGBoost scales boosting by turning gradient statistics into regularized decisions about leaves and splits.
Trees, forests & gradient boostingCalibrating and explaining ensembles
Calibrate probabilities for decisions, and read explanations as model accounting—not causal proof.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.