Gradient boosting algorithms

Gradient boosting is a type of ensemble learning algorithm used for classification and regression tasks. The algorithm works by building an ensemble of weak prediction models, typically decision trees, that are trained sequentially to minimize a loss function.

In gradient boosting, each new model in the ensemble is trained to predict the residual error of the previous model, so that the ensemble as a whole gradually improves its accuracy. This is done by using gradient descent to minimize a loss function, such as mean squared error or cross-entropy loss.

Gradient boosting has several advantages over other machine learning algorithms, including its ability to handle heterogeneous data types, its ability to automatically handle missing data and outliers, and its ability to provide interpretable feature importance scores.

Gradient boosting has been successfully used in various applications, including speech recognition, natural language processing, and image recognition.

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