Home▸AI & Machine Learning▸Legal Case Outcome Predictor — Random Forest Live (2D)

Legal Case Outcome Predictor — Random Forest Live (2D)

A real random forest of bootstrapped decision trees votes live on simulated legal case features — precedent strength, evidence score, jurisdiction and complexity — with per-tree votes converging to an ensemble outcome probability, rendered as a plain 2D grid.

AI & Machine Learning2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion runs the identical bootstrapped random-forest engine as the 3D version, rendered as a flat grid of trees instead of an orbiting scene: tune the case's precedent, evidence, jurisdiction and complexity and watch every tree revote instantly, or change the forest's tree count, depth and feature-subset size and retrain from scratch. Each cell is one real decision tree, grown on its own bootstrap resample with a random feature subset per split, and the out-of-bag accuracy is computed genuinely from cases each tree never trained on.

⚙ Under the hood

2D random forest of bootstrapped decision trees votes live on simulated legal case features — precedent, evidence, jurisdiction, complexity — with per-tree votes converging to an ensemble probability.

random forestdecision treebootstrap aggregatinggini impurityout-of-bag accuracylegal AIensemble learning

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