Minimal self-contained example for setting up TMVA with binary classification.

This is intended as a simple foundation to build on. It assumes you are familiar with TMVA already. As such concepts like the Factory, the DataLoader and others are not explained. For descriptions and tutorials use the TMVA User's Guide ( under TMVA) or the more detailed examples provided with TMVA e.g. TMVAClassification.C.

Sets up a minimal binary classification example with two slightly overlapping 2-D gaussian distributions and trains a BDT classifier to discriminate the data.

  • Project : TMVA - a ROOT-integrated toolkit for multivariate data analysis
  • Package : TMVA
  • Root Macro: TMVAMinimalClassification.C

Author: Kim Albertsson
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, August 15, 2022 at 09:48 AM.

In [ ]:
%%cpp -d
#include "TMVA/DataLoader.h"
#include "TMVA/Factory.h"

#include "TFile.h"
#include "TString.h"
#include "TTree.h"

Helper function to generate 2-D gaussian data points and fill to a ROOT TTree.

Arguments: nPoints Number of points to generate. offset Mean of the generated numbers scale Standard deviation of the generated numbers. seed Seed for random number generator. Use seed=0 for random seed. Returns a TTree ready to be used as input to TMVA.

In [ ]:
%%cpp -d
TTree *genTree(Int_t nPoints, Double_t offset, Double_t scale, UInt_t seed = 100)
   TRandom rng(seed);
   Double_t x = 0;
   Double_t y = 0;

   TTree *data = new TTree();
   data->Branch("x", &x, "x/D");
   data->Branch("y", &y, "y/D");

   for (Int_t n = 0; n < nPoints; ++n) {
      x = rng.Rndm() * scale;
      y = offset + rng.Rndm() * scale;

    Important: Disconnects the tree from the memory locations of x and y.
   return data;
In [ ]:
TString outputFilename = "out.root";
TFile *outFile = new TFile(outputFilename, "RECREATE");

Data generation

In [ ]:
TTree *signalTree = genTree(1000, 0.0, 2.0, 100);
TTree *backgroundTree = genTree(1000, 1.0, 2.0, 101);

TString factoryOptions = "AnalysisType=Classification";
TMVA::Factory factory{"", outFile, factoryOptions};

TMVA::DataLoader dataloader{"dataset"};

Data specification

In [ ]:
dataloader.AddVariable("x", 'D');
dataloader.AddVariable("y", 'D');

dataloader.AddSignalTree(signalTree, 1.0);
dataloader.AddBackgroundTree(backgroundTree, 1.0);

TCut signalCut = "";
TCut backgroundCut = "";
TString datasetOptions = "SplitMode=Random";
dataloader.PrepareTrainingAndTestTree(signalCut, backgroundCut, datasetOptions);

Method specification

In [ ]:
TString methodOptions = "";
factory.BookMethod(&dataloader, TMVA::Types::kBDT, "BDT", methodOptions);

Training and Evaluation

In [ ]:

Clean up

In [ ]:

delete outFile;
delete signalTree;
delete backgroundTree;