XGBoost explained • /r/MachineLearning


To expand: according to my naive understanding, boosted trees are basically an ensemble of decision trees which are fit sequentially so that each new tree makes up for the errors of the previously existing set of trees. The model is "boosted" by focusing new additions on correcting the residual errors of the last version of the model. The "gradient" comes in afterward as the parameters of the tree ensemble are optimized to minimize the error of the whole "base learner". I think of this as fine tuning of the boosted tree ensemble using a gradient-based optimization.

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