Random Forest from Scratch

Build a complete Random Forest classifier from impurity math to a bagged ensemble with bootstrapping and feature subsampling.

5 parts15 individual lessons1.3 estimated hours75 XP available

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Every source step is its own lesson with intuition, concepts, correctly rendered MathJax mathematics, implementation, tests, mistakes, and a checkpoint.

PART 1

Impurity and Splitting Primitives

Build the low-level tools a decision tree needs: measuring impurity, partitioning data on a feature threshold, and scoring candidate splits.

0/3

001impurity+5 XP 002split dataset+5 XP 003split score+5 XP

PART 2

Growing a Decision Tree

Combine the primitives into a recursive tree builder with best-split selection, stopping criteria, and leaf prediction.

0/4

004best split+5 XP 005should stop+5 XP 006leaf prediction+5 XP 007build tree+5 XP

PART 3

Predicting with a Single Tree

Traverse a fitted tree to predict single examples and full datasets.

0/2

008predict example tree+5 XP 009predict tree+5 XP

PART 4

Randomness for the Ensemble

Add bootstrap sampling of rows and random feature subsets so that individual trees become decorrelated.

0/2

010bootstrap sample+5 XP 011feature subset+5 XP

PART 5

Training and Predicting with the Forest

Grow many trees on randomized data, aggregate their votes, produce forest-level predictions, and evaluate accuracy.

0/4

012train forest+5 XP 013combine predictions+5 XP 014predict forest+5 XP 015accuracy+5 XP