Gradient boosting machines (GBMs) are a type of ensemble learning technique that builds predictive models by combining the strengths of multiple weak learners, typically decision trees. They work by fitting new models to the residual errors made by existing models in a sequential manner, which helps improve overall prediction accuracy. GBMs are particularly effective for regression and classification tasks due to their flexibility and ability to handle different types of data.
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