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What is Understanding Ensemble Learning Methods about?

Ensemble learning uses multiple learning algorithms together to obtain better predictive performance than could be achieved by any individual algorithm alone. It combines multiple hypotheses to form a better hypothesis. Common ensemble methods include bagging, boosting, and Bayesian model averaging, which average the predictions of models weighted by their posterior probabilities. Empirically, ensembles tend to perform best when the individual models are diverse, though more computation is required to evalu

Author

joshuapeter961204

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EN