Ensemble Methods Comparator

Compare bagging, boosting, and stacking to see how combining models improves predictions

Overview

The Ensemble Methods Comparator demonstrates how combining multiple weak learners creates stronger predictive models. Compare bagging (parallel training with averaging), boosting (sequential training focusing on errors), and stacking (using a meta-model to combine predictions) to understand their different approaches and performance characteristics.

Tips

  1. Start with 3-5 weak learners and observe how combining them improves accuracy over any individual model
  2. Compare all three methods on the same dataset to see how bagging reduces variance, boosting reduces bias, and stacking often achieves the best performance
  3. Test on imbalanced data to understand when stratified approaches are critical for fair evaluation
  4. Watch individual model contributions to see how different methods leverage the base learners (parallel vs sequential learning)
  5. Experiment with the number of estimators - too few won’t capture the ensemble benefit, but returns diminish beyond a certain point