Ensemble Learning
Combining multiple models to make predictions, typically improving performance over individual models.
In more detail
Ensemble methods include bagging, boosting, and stacking. Combines models through voting (classification) or averaging (regression). Often achieves better performance, especially in competitions. Examples: Random Forests, Gradient Boosting, XGBoost.
Examples
- Random Forests
- Gradient Boosting (XGBoost, LightGBM)
- Voting Classifiers
- Stacking
Related terms
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