Development of Machine Learning Tools in ROOT

17th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

物理学;计算机科学

Gleyzer, S.V.^1 ; Moneta, L.^2 ; Zapata, Omar A.^3

University of Florida, United States^1

CERN, Switzerland^2

University of Antioquia, Metropolitan Institute of Technology, Colombia^3

关键词: Cross validation;    Integrated toolkit;    Large-scale data analysis;    LHC experiments;    Machine learning software;    Modular designs;    Multi variate analysis;    Variable importances;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/762/1/012043/pdf
DOI  :  10.1088/1742-6596/762/1/012043

学科分类：计算机科学（综合）

来源: IOP

【 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.