Home▸Machine Learning & Neural Networks▸Random Forest Classifier (2D)

🌳 Random Forest Classifier (2D)

Interactive 2D random forest classifier: train bootstrap-sampled decision trees with random feature subsampling and watch the ensemble decision-region overlay form from majority vote.

Machine Learning & Neural Networks2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

Details

CategoryMachine Learning & Neural Networks

RenderingCanvas 2D

DifficultyModerate

Target FPS60

ControlsDataset shape (Moons/Circles/Blobs/XOR), Noise slider, Points N slider, Generate random clusters button, Tree count slider, Max depth slider, Features-per-split (1/sqrt/all), Criterion select (Gini/entropy), View forest vs single tree toggle, Tree index slider, Click canvas to add training point (left=class 0, right=class 1), Retrain button, Reset points button

machine learningrandom forestdecision treebaggingensemble learningclassification

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⚙ Under the hood

Train an ensemble of bootstrap-sampled decision trees on 2D labeled points and watch their majority-vote decision regions blend into one forest boundary. Adjust tree count, max depth, and per-split feature sampling, then compare a single overfit tree against the full ensemble.

machine learningrandom forestdecision treebaggingensemble learningclassification

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install