What are some examples of ensemble learning techniques?
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Examples of ensemble learning techniques include bagging, boosting, and random forests.
1. Random Forest: A collection of decision trees trained on random subsets of the data and features, where predictions are aggregated to improve accuracy and reduce overfitting.
2. Gradient Boosting Machines (GBM): A sequential ensemble method where weak learners (usually decision trees) are added sequentially, with each new learner correcting the errors made by the previous ones.
3. AdaBoost (Adaptive Boosting): An iterative ensemble method that assigns weights to misclassified data points and trains subsequent models to focus on correcting these errors.
4. Bagging (Bootstrap Aggregating): A technique that builds multiple models independently on different subsets of the training data and combines their predictions through averaging or voting.
5. Stacking: Combines multiple base models by training a meta-model (or blender) on their predictions to make final predictions.
6. XGBoost (Extreme Gradient Boosting): An optimized implementation of gradient boosting that leverages tree pruning, regularization, and parallel processing to improve training speed and accuracy.
7. LightGBM: Another implementation of gradient boosting that uses a novel gradient-based approach to split decision trees, resulting in faster training and higher efficiency.