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Notes on using Multivariate Analysis methodsIntroductionThis page is a repository of some useful information for applying multivariate (MVA) methods to data analysis in particle physics. It is provided as support for students carrying out projects in the context of the ATLAS experiment at CERN, using the ROOT TMVA framework.ROOT TMVA resources
General references on multivariate analysis methods
Possible lines of investigationType of classifierCompare a simple cut-by-cut analysis performance with that of the following multivariate methods (all using the same input variables):
Weak input variablesSome classifiers (such as BDTs) are known to be more robust than others (such as NNs), with respect to the inclusion of weak input variables (i.e. variables with very low signal-background discriminating power). Test this by training a classifier using, for instance, 4 strong variables and 4 very weak variables. Train a new classifier using only the 4 strongest variables as inputs, and compare their performance. Do this for eg an NN, and then for a BDT.Impact of architecture and trainingHow much does the performance of a given classifier type depend on the configuration of its internal parameters, or of the training steps? To investigate this for eg an NN, use as reference its performance out-of-the-box (i.e. using the default TMVA options).
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