TMVA is a tool to run a multivariate analysis on a root tree. It is included in ROOT from version release 5.34/11 on. More information can be found in:

• Main website
• Manual

When using the neural netwerk method MLP, you might need ROOT 34.0.0 or newer, to have larger buffer for the xml reader, for example:

. /afs/cern.ch/sw/lcg/app/releases/ROOT/5.34.26/x86_64-slc6-gcc48-opt/root/bin/thisroot.sh

The parameters and options of the MVA method can be optimized from the default settings for better a performance, see the reference page.

For the BDT important parameters are the the learning rate, number of boost steps and maximal tree depth:

• AdaBoostBeta=0.5: learning rate, smaller (~0.1) is better, but takes longer
• nTrees=800: number of boost steps, too large mainly costs time and can cause overtraining
• MaxDepth=3: maximum tree depth, ~2-5 depending on interaction of the variables
• nCuts=20: grid points in variable range to find the optimal cut in node splitting
• MinNodeSize=5%

Important MLP parameters to tune are the number of neurons on each hidden layer, learning rate and the activation function.

• HiddenLayers=N,N-1: number of nodes in each hidden layer for N variables
  • N = one hidden layer with N nodes
  • N,N = two hidden layers
  • N+2,N = two hidden layers, with N+2 nodes in the first
• LearningRate=0.02

• TMVA tutorial on Indico
• PyRoot example