Autor:	Pakbin, Arash, Wang, Xiaochen, Mortazavi, Bobak J., Lee, Donald K. K.
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED. Comment: 27 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.12591 Zobrazit plný text záznamu View this record from Arxiv