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Ensemble learning

Ensemble Learning is a method of reaching a consensus in predictions by fusing the salient properties of two or more models.

The final ensemble learning framework is more robust than the individual models that constitute the ensemble because ensembling reduces the variance in the prediction errors. Ensemble Learning tries to capture complementary information from its different contributing models—that is, an ensemble framework is successful when the contributing models are statistically diverse.

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