XGBoost (eXtreme Gradient Boosting) is a scalable, end-to-end, tree-boosting system that has produced state-of-the-art results on many machine learning challenges.

A gradient boosting machine model performed best among five machine learning models tested for predicting delirium, according to findings recently published in JAMA Network Open. “Existing ...

Extreme Gradient Boosting (XGBoost) provided the best performance in each paper in which it was tested. Numerous heterogeneities exist, including definition of “injury”, granularity of data and scope ...

Estimation is conducted by a componentwise gradient boosting algorithm, which scales well to large data sets and complex models. Applied Statistics of the Journal of the Royal Statistical Society was ...

The best-performing model for the classification of 1-year OS was the extreme gradient boosting algorithm, with AUC and F1-score values equal to 0.805 and 0.802, respectively.

SIAM Journal on Numerical Analysis, Vol. 15, No. 6 (Dec., 1978), pp. 1247-1257 (11 pages) This paper studies the convergence of a conjugate gradient algorithm proposed in a recent paper by Shanno. It ...