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Ensembling (sometimes ensemble learning) is a class of meta-algorithmic techniques where multiple models are trained and their results are aggregated to improve classification performance. It is effective in a wide variety of problems.
Two commonly used methods are:
- boosting: a method of weighting the predictions of multiple models such that the combined prediction is more accurate than each individual model
- bagging: also known as bootstrap aggregating, where multiple models are trained on random subsets of the entire training dataset, and the results of these models are averaged