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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, and stacking, each with its own unique approach to combining models. Bagging, for example, involves training multiple models in parallel and aggregating their predictions through a voting mechanism. Boosting, on the other hand, focuses on training models sequentially, with each new model correcting the errors of its predecessor. Ensemble methods are particularly useful when dealing with complex and noisy data, as they can help improve the overall robustness and generalization of the model. By combining multiple models, ensemble methods can also reduce the risk of overfitting, where a model performs well on the training data but poorly on unseen data.- Ensemble methods are a valuable tool in the machine learning toolbox, offering a way to harness the collective intelligence of multiple models for better predictions. Whether you're working on classification, regression, or any other machine learning task, ensemble methods can help improve the accuracy and reliability of your models.
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