Gradient boosting machines (GBM) are a powerful ensemble learning technique used for regression and classification tasks, which builds models in a sequential manner by optimizing the performance of weak learners. Each new model added to the ensemble focuses on correcting the errors made by the previous models, leading to improved predictive accuracy. This approach allows GBMs to effectively combine multiple weak models into a strong predictive model, making them highly effective in handling complex datasets.
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