Development of Machine Learning Tools in ROOT

Abstract

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.