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Ensemble methods combine multiple models to improve predictive performance from "summary" of Machine Learning by Stephen Marsland
Ensemble methods take a different approach to machine learning by combining multiple models to enhance predictive performance. This approach is based on the idea that a group of models working together can outperform any individual model. In ensemble methods, each model in the group is trained independently on the same dataset, but with different parameters or using different algorithms. There are different types of ensemble methods, including bagging, boosting, and stacking. Bagging involves training multiple models in parallel and then averaging their predictions to make the final prediction. Boosting, on the other hand, focuses on se...
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