TMVA, the Toolkit for Multivariate Data Analysis with ROOT

Multivariate classification methods based on machine learning techniques play a fundamental role in today’s high-energy physics analyses dealing with ever smaller signal in ever larger data sets. TMVA is a toolkit integrated in the ROOT framework which implements a large variety of multivariate classification algorithms ranging from simple rectangular cut optimisation and likelihood estimators, over linear and non-linear discriminants to more recent developments like boosted decision trees, rule fitting and support vector machines. All classifiers can be trained, tested and evaluated simultaneously. They all see the same training are then afterwards also tested on the same independent test data allowing meaningful comparisons between the methods for a particular use case. Here, an overview about the package and the classifiers currently implemented is presented.

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