Ensemble Methods: Bagging vs Boosting - Interactive Visualization

Ensemble methods combine multiple models so that their collective prediction is better than any individual model. Bagging reduces variance by training models in parallel on bootstrapped data and averaging results - each model is independent. Boosting reduces bias by training models sequentially, each correcting the previous model's errors. Stacking learns how to best combine model outputs using a meta-learner.

• Visualize parallel training in bagging vs sequential training in boosting - the key structural difference
• See how bagging improves on unstable high-variance models (like deep trees) while boosting improves low-variance, high-bias models
• Understand model diversity: why having different kinds of errors across ensemble members is the secret to strong performance
• Explore stacking: a meta-learner trained on out-of-fold predictions from base models - often a few points better than simple averaging
• Learn when to use each method: bagging for noise robustness, boosting for maximum accuracy on clean data

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