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Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent m...

Subjects / keywords: Gradient boosting; Parameterized complexity; Mathematics; Robustness (evolution); Boosting (machine learning); Regression; Mathematical optimization; Algorithm; Data point; Computer science; Statistics; Artificial intell...

Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a we...

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-...