100 Facts About Ensemble Methods

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Here’s a list of 100 facts about ensemble methods:

1. Ensemble methods combine the predictions of multiple models to improve overall performance.
2. The concept of “wisdom of the crowd” underlies the effectiveness of ensemble methods.
3. Ensemble methods can reduce overfitting and increase model generalization.
4. Bagging (Bootstrap Aggregating) is a common ensemble technique that builds multiple models using different subsets of the training data.
5. Random Forest is an ensemble method based on bagging, using decision trees as base models.
6. Decision trees are often used as base models in ensemble methods due to their simplicity and interpretability.
7. Boosting is another ensemble technique that focuses on sequentially training models, giving more weight to misclassified instances.
8. AdaBoost (Adaptive Boosting) is a popular boosting algorithm that adjusts the weights of misclassified instances.
9. Gradient Boosting builds models sequentially, minimizing the residual errors of the previous models.
10. XGBoost is an optimized implementation of gradient boosting that often outperforms traditional implementations.
11. LightGBM and CatBoost are other popular gradient…