Boosting is a popular technique in machine learning used to improve the performance of a weak learner by combining the predictions of several models.
In boosting, a set of weak classifiers are trained iteratively, where each classifier is trained on a subset of the data, with the aim of improving the overall performance of the model. The weak classifiers are typically simple models that have low accuracy, such as decision trees.
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