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Many ensemble methods, therefore, seek to promote diversity among the models they combine. Fast algorithms such as decision trees are commonly used in ensemble methods (for example, random forests), although slower algorithms can benefit from ensemble techniques as well.īy analogy, ensemble techniques have been used also in unsupervised learning scenarios, for example in consensus clustering or in anomaly detection.Įmpirically, ensembles tend to yield better results when there is a significant diversity among the models. An ensemble system may be more efficient at improving overall accuracy for the same increase in compute, storage, or communication resources by using that increase on two or more methods, than would have been improved by increasing resource use for a single method. On the other hand, the alternative is to do a lot more learning on one non-ensemble system. In one sense, ensemble learning may be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation. Įvaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model. The broader term of multiple classifier systems also covers hybridization of hypotheses that are not induced by the same base learner. The term ensemble is usually reserved for methods that generate multiple hypotheses using the same base learner. Ensembles combine multiple hypotheses to form a (hopefully) better hypothesis. Even if the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Supervised learning algorithms perform the task of searching through a hypothesis space to find a suitable hypothesis that will make good predictions with a particular problem. 5 Implementations in statistics packages.