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Gradient boosting, a powerful ensemble-learning technique, enhances the accuracy of models by sequentially combining weak learner, usually decision trees, into a strong prediction model. It works on the principle that minimizing errors is achieved through gradient descent. This helps to refine predictions iteratively. This method is used widely in regression and classification tasks because it reduces bias and variance and improves predictive performance. Data Science Training in Pune
Gradient boosting is a method of building models that builds them in stages, with each tree correcting the mistakes made by the previous one. In the beginning, a simple, single-decision tree model is used to make predictions. The residuals are the difference between the predictions and actual values. This is used to train the new model. Gradient boosting allows the model to learn from previous mistakes, instead of fitting it directly to the variable. The process is repeated, and each successive model reduces the error even further until a…
