| TMVA - Toolkit for Multivariate Data Analysis with ROOT: Users guide (2007) | |||||||||
Abstract | |||||||||
| Multivariate machine learning techniques for the classification of data from high-energy physics experiments have become a fundamental ingredient to most analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a ROOT-integrated toolkit, which hosts a large variety of multivariate classification algorithms ranging from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA allows the simultaneous training, testing and performance evaluation of all these classifiers with user-friendly interfaces. | |||||||||
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