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Ensemble models are models consisting of multiple models or algorithms. Individual models can be combined through various methods such as bagging, boosting and stacking. Ensemble modeling can reduce variance, minimize modeling method bias and thus decrease chances of overfitting. The predictions based on ensemble modelling methods tend to be more stable with lower variance. An exciting application of ensemble modelling comes from the public health body in the U.S., who started crowdsourcing forecasting models in a "Predict Influenza Season Challenge". They then combined them into an ensemble for better accuracy.
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