The Toolkit for Multivariate Data Analysis with ROOT (TMVA) provides a machine learning environment for the processing and evaluation of multivariate classification, both binary and multi-class, and regression techniques targeting applications in high-energy physics. The package includes:
- Neural networks
- Deep networks
- Multilayer perceptron
- Boosted/Bagged decision trees
- Function discriminant analysis (FDA)
- Multidimensional probability density estimation (PDE - range-search approach)
- Multidimensional k-nearest neighbor classifier
- Predictive learning via rule ensembles (RuleFit)
- Projective likelihood estimation (PDE approach)
- Rectangular cut optimisation
- Support Vector Machine (SVM)
For TMVA, topical manuals are available at Topical Manuals - TMVA.
They contain in-depth information about TMVA.
TMVA in the ROOT forum
Discuss TMVA in the ROOT forum.
The ROOT tutorials for TMVA, available in
$ROOTSYS/tutorials/tmva, provide example jobs for the training phase and the application of the training results in a classification or regression analysis using the TMVA Reader.
TMVAClassification.C provides examples for the training and testing of TMVA classifiers.
TMVAMulticlass.C provides an example for the training and testing of a TMVA multi-class classification.
TMVARegression.C provides examples for the training and testing of TMVA classifiers.
TMVAClassificationApplication.C provides an example on how to use the trained classifiers within an analysis module.
TMVAMulticlassApplication.C provides an example on how to use the trained multi-class classifiers within an analysis module.
TMVARegressionApplication.C provides an example on how to use the trained regression MVAs within an analysis module.
TMVAClassification.C uses an academic toy data set for training and testing. It consists of four linearly correlated, Gaussian distributed discriminating input variables, with different sample means for signal and background.
The training job provides a formatted output logging that contains the following information:
- Linear correlation matrices for the input variables.
- Correlation ratios and mutual information between input variables and regression targets.
- Variable ranking.
- Summaries of the MVA configurations.
- Goodness-of-fit evaluation for PDFs.
- Signal and background (or regression target) correlations between the various MVA methods.
- Decision overlaps.
- Signal efficiencies at benchmark background rejection rates.
- Other performance estimators.
After a successful training, TMVA provides so called “weight”-files (here in the
TMVA.root ROOT file) that contain all information necessary to recreate the method without retraining.
In addition, a GUI is provided to execute macros for displaying training, test and evaluation results.
Figure: GUI for TMVA.
You can, for example, plot input variable distributions.
Figure: Example plots for input variable distributions.