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Learning stops when the algorithm achieves an acceptable level of performance. Ensemble learning are methods that combine the predictions from multiple models.


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It is important in ensemble learning that the models that comprise the ensemble are good making different prediction errors.

. Predictions that are good in different ways can result in a prediction that is both more stable and often better than the predictions of any individual. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers the algorithm iteratively makes predictions on the training data and is corrected by the teacher.


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