What is gradient boosting in machine learning?
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Gradient boosting is an ensemble learning technique that combines multiple weak learners, typically decision trees, to create a strong predictive model. It sequentially improves upon the predictions of the weak learners by fitting each subsequent model to the residuals of the previous models. It is known for its high predictive accuracy and robustness to overfitting, making it widely used in regression and classification tasks.
Gradient boosting is an ensemble learning technique that builds a series of weak learners sequentially, with each learner correcting the errors of the previous ones by fitting to the residuals.