In predictive modeling, boosting is an iterative ensemble method that starts out by applying a classification algorithm and generating classifications. The classifications are then assessed, and a second round of model-fitting occurs in which the records classified incorrectly in the first round are given a higher weight in the second round. This procedure is repeated a number of times, and the final classifier results from a merger of the various iterations, with lesser weights typically accorded to the very last rounds. The idea is to concentrate the iterative learning process on the hard-to-classify cases.
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