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Ensemble methods combine multiple models to improve prediction accuracy from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Ensemble methods refer to the practice of combining multiple models to improve prediction accuracy. Instead of relying on a single model to make predictions, ensemble methods leverage the strength of a diverse set of models to arrive at a more accurate prediction. Each model in the ensemble may have its own strengths and weaknesses, which can be mitigated by combining them with other models that complement their performance. There are several ways to create an ensemble of models. One common approach is to train multiple models on different subsets of the data and then combine their predictions using a technique such as averaging or a voting mechanism. This allows the ensemble to benefit from the diversity of the individual models, as errors made by one model may be offset by the correct predictions of another. Another popular method of creating an ensemble is through the use of bagging...
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