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Ensemble methods combine multiple models to enhance prediction accuracy from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Ensemble methods are techniques that combine multiple models to enhance prediction accuracy. They can be used in a variety of machine learning tasks, such as classification and regression. By aggregating the predictions of multiple models, ensemble methods are able to produce more reliable and accurate results than any single model on its own. One common type of ensemble method is the bagging meta-estimator, which works by training multiple instances of the same base estimator on different subsets of the training data. These individual models are then combined by averaging their predictions, resulting in a final prediction that is more robust and less prone to overfitting. Bagging can be particularly effective when the base estimator is unstable or has high variance. Another popular ensemble method is the random forest, which is a specific implementation of bagging using decision trees as the base estimator. Random forests are able to reduce the variance of individual decision trees by introducing randomness into the training...
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