Very fast Bayesian Additive Regression Trees on GPU

Autor:	Petrillo, Giacomo
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique based on an ensemble of decision trees. It is part of the toolbox of many statisticians. The overall statistical quality of the regression is typically higher than other generic alternatives, and it requires less manual tuning, making it a good default choice. However, it is a niche method compared to its natural competitor XGBoost, due to the longer running time, making sample sizes above 10,000-100,000 a nuisance. I present a GPU-enabled implementation of BART, faster by up to 200x relative to a single CPU core, making BART competitive in running time with XGBoost. This implementation is available in the Python package bartz. Comment: Check out the software at https://github.com/Gattocrucco/bartz
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.23244 Zobrazit plný text záznamu View this record from Arxiv