Abstract
The ensemble technique relies on the idea that aggregation of many classifiers and regressors will lead to a better prediction [1]. In this chapter, we will introduce the ensemble technique and cover two ways in which to organize an ensemble (literally, a set) of machine learning methods called voting and bagging [2] and one algorithm to perform bagging called random forest [1, 3]. The other two ways to organize the ensemble methods are called boosting and stacking, which will be covered in the next chapter.
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El Morr, C., Jammal, M., Ali-Hassan, H., El-Hallak, W. (2022). Voting and Bagging. In: Machine Learning for Practical Decision Making. International Series in Operations Research & Management Science, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-031-16990-8_14
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DOI: https://doi.org/10.1007/978-3-031-16990-8_14
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