Web Reference: Feb 18, 2026 · Boosting is an ensemble learning technique that improves predictive accuracy by combining multiple weak learners into a single strong model. It works iteratively where each new model focuses on correcting the mistakes of its predecessors and gradually improves overall performance. While boosting is not algorithmically constrained, most boosting algorithms consist of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier. Find out what is boosting, how it works with AI/ML, and how to use boosting in machine learning on AWS.
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