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Ensemble methods combine multiple models for better predictions from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Ensemble methods are a powerful approach in machine learning that involves combining multiple models to improve overall prediction accuracy. These methods work on the premise that by leveraging the strengths of different models, the weaknesses of individual models can be mitigated. The basic idea behind ensemble methods is to create a diverse set of models that are trained on the same dataset but with different algorithms or subsets of data. By combining the predictions of these diverse models, the ensemble can make more accurate predictions than any single model on its own. There are different types of ensemble methods, such as bagging, boosting, a...
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