TMVA Toolkit for Multivariate Data Analysis with ROOT
From the website:
The Toolkit for Multivariate Analysis (TMVA) provides a ROOT-integrated machine learning environment for the processing and parallel evaluation of multivariate classification and regression techniques. TMVA is specifically designed to the needs of high-energy physics (HEP) applications, but should not be restricted to these. The package includes:
- Rectangular cut optimisation
- Projective likelihood estimation (PDE approach)
- Multidimensional probability density estimation (PDE
– range-search approach and PDE-Foam)- Multidimensional k-nearest neighbour method
- Linear discriminant analysis (H-Matrix, Fisher and linear (LD) discriminants)
- Function discriminant analysis (FDA)
- Artificial neural networks (three different MLP
implementations)- Boosted/Bagged decision trees
- Predictive learning via rule ensembles (RuleFit)
- Support Vector Machine (SVM)
TMVA consists of object-oriented implementations in C++ for each of these multivariate methods and provides training, testing and performance evaluation algorithms and visualization scripts. The MVA training and testing is performed with the use of user-supplied data sets in form of ROOT trees or text files, where each event can have an individual weight. The true event classification or target value (for regression problems) in these data sets must be known. Preselection requirements and transformations can be applied on this data. TMVA supports the use of variable combinations and formulas.
Questions:
- Review TMVA documentation on one method in detail.
- Using a topic map, demonstrate supplementing that documentation with additional literature or examples.
- TMVA is not restricted to high energy physics but do you find citations of its use outside of high energy physics?