Boosting

I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

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In machine learning, boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can improve the stability and accuracy of ML classification and regression algorithms. Hence, it is prevalent in supervised learning for converting weak learners to strong learners.

A weak learner is defined as a classifier that is only slightly correlated with true classification. A strong learner is a classifier that is arbitrarily well-correlated with the true classification. While boosting is not algorithmically constrained, most boosting algorithms consists of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier.

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