Random Forest Ensemble - Interactive Visualization

A random forest builds hundreds of decision trees on bootstrapped subsets of the data, each considering only a random subset of features at each split. Predictions are made by majority vote (classification) or averaging (regression). The randomness decorrelates the trees so that averaging their predictions dramatically reduces variance without increasing bias.

• Watch the decision boundary become smoother and more stable as each new tree is added to the ensemble
• See feature importance scores computed from how much each feature reduces impurity across all trees
• Understand out-of-bag (OOB) error: free validation without a separate test set, using samples not in each bootstrap
• Learn how bagging reduces variance: individual trees overfit, but their average does not
• Compare a single deep tree vs a 100-tree forest - the forest wins on unseen data consistently

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.