Toolkit for multivariate data analysis
The Toolkit for Multivariate Analysis (TMVA) provides a ROOT-integrated environment for the parallel processing and evaluation of MVA techniques to discriminate signal from background samples. It presently includes (ranked by complexity): * Rectangular cut optimization * Correlated likelihood estimator (PDE approach) * Multi-dimensional likelihood estimator (PDE - range-search approach) * Fisher (and Mahalanobis) discriminant * H-Matrix (chi-squared) estimator * Artificial Neural Network (two different implementations) * Boosted Decision Trees The TMVA package includes an implementation for each of these discrimination techniques, their training and testing (performance evaluation). In addition all these methods can be tested in parallel, and hence their performance on a particular data set may easily be compared.
You can contact the maintainers of this package via email at
root dash maintainers at fedoraproject dot org.