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Boosting

Boosting

boosting:
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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Predictive Analytics 1 – Machine Learning Tools
This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.
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