Gradient boosting machines are a type of machine learning algorithm used for supervised learning tasks, particularly in regression and classification problems. They build models in a sequential manner, where each new model corrects the errors made by the previous ones, thus improving overall predictive performance. This method focuses on minimizing a specified loss function using gradient descent, leading to a powerful ensemble of weak learners that can capture complex patterns in the data.
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