Tag Archives: GBDT

Gradient Boosting Decision Tree

In the previous article, we've talked about AdaBoost which combines output of weak learners into a weighted sum that represents the final output of the boosted classifier. If you know little about AdaBoost or additive model, we highly recommend you read the article first. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. Boosting Tree Boosting tree is based on additive model which can be repsentated as following: \(\begin{align}f_M(x) = \sum_{m=1}^{M} T(x; \theta_m)\end{align}\) where \(T(x; \theta_m)\)

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