Development of Machine Learning Tools in ROOT

被引：0

作者：

Gleyzer, S., V ^{[1
]}

Moneta, L. ^{[2
]}

Zapata, Omar A. ^{[3
,4
]}

机构：

[1] Univ Florida, Gainesville, FL 32611 USA

[2] CERN, Geneva, Switzerland

[3] Univ Antioquia, Medellin, Colombia

[4] Metropolitan Inst Technol, Medellin, Colombia

来源：

17TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH (ACAT2016) | 2016年 / 762卷

关键词：

D O I：

10.1088/1742-6596/762/1/012043

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

ROOT is a framework for large-scale data analysis that provides basic and advanced statistical methods used by the LHC experiments. These include machine learning algorithms from the ROOT-integrated Toolkit for Multivariate Analysis (TMVA). We present several recent developments in TMVA, including a new modular design, new algorithms for variable importance and cross-validation, interfaces to other machine-learning software packages and integration of TMVA with Jupyter, making it accessible with a browser.

引用

页数：7

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