Lorenzo Moneta (CERN) Omar Andres Zapata Mesa (Metropolitan Institute of Technology)
ROOT, a data analysis framework, provides advanced statistical methods needed by the LHC experiments for analyzing their data. These include machine learning tools required for classification, regression and clustering. These methods are provided by the TMVA, a toolkit for multi-variate analysis within ROOT. We will present recent development in TMVA and new interfaces between ROOT and TMVA and other well known statistical tools based on R and Python. We will show a new modular design of TMVA, giving users a lot of flexibility, novel features for cross-validation, variable selection and parallelism.
Lorenzo Moneta (CERN) Omar Andres Zapata Mesa (Metropolitan Institute of Technology) Dr Sergei Gleyzer (University of Florida (US))