Gradient Boosting
Gradient Boosting is a machine learning technique that builds an ensemble of weak prediction models, such as decision trees, to create a strong predictive model. It is an iterative process that starts with a simple model and sequentially improves it by adding new models that focus on the errors made by the previous models. In each iteration, the algorithm calculates the gradient of the loss function with respect to the predictions made by the current model and fits a new model to the gradient. The final model is a weighted sum of all the weak models, where the weights are determined by the performance of each model. Gradient Boosting is widely used in various applications, such as regression, classification, and ranking.
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