Train a random forest on 2D data — visualize decision boundaries, individual trees, and feature importance
Settings
5
4
70%
10%
Acc: —
Random Forest: Ensemble of CART decision trees, each trained on a bootstrap sample. Prediction = majority vote.
Randomness in both samples (bagging) and features (random feature subset at each split) reduces variance and prevents overfitting.