XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m
The main features of hammerlab/survivalstan are: Survival Analysis.
Open-source alternatives to hammerlab/survivalstan include: autonlab/auton-survival. autonlab/deepsurvivalmachines. better/convoys. dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… sebp/scikit-survival — Survival analysis built on top of scikit-learn. square/pysurvival.