Abstract
Ensemble ML methods build predictive models by inducing several different predictors, called base predictors or base learners. Typically, a base predictor is a very simple model that is not meant to work on its own. The predictions of such simple models are normally not much better than random guesses, which is why they are called weak learners. It is through the aggregation of the different answers from the various base learners that ensemble learning builds strong learners that produce accurate predictions.
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Vanneschi, L., Silva, S. (2023). Ensemble Methods. In: Lectures on Intelligent Systems. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-031-17922-8_11
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DOI: https://doi.org/10.1007/978-3-031-17922-8_11
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