The ensemble is a way of bringing together a varied group of learners in order to improve the model’s stability and predictive capability. Ensemble learning approaches may be divided into two categories:
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Bagging: The tagging approach allows you to apply similar learners to a limited group of people. It enables you to make more accurate forecasts.
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Boosting: Boosting is an iterative approach for adjusting the weight of observation in relation to the previous classification. Boosting reduces bias error and aids in the development of robust prediction models.