void Train() {
  TFile* outputFile = TFile::Open("output.root","RECREATE");
  TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification");
  TFile* inputFile = new TFile("dataSchachbrett.root");
  TTree* sig = (TTree*)inputFile->Get("TreeS");
  TTree* bkg = (TTree*)inputFile->Get("TreeB");
  double sigWeight = 1.0; double bkgWeight = 1.0;
  TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
  dataloader->AddSignalTree(sig, sigWeight);
  dataloader->AddBackgroundTree(bkg, bkgWeight);
  dataloader->AddVariable("var0", 'F');
  dataloader->AddVariable("var1", 'F');
  TCut mycut = "";
  dataloader->PrepareTrainingAndTestTree(mycut,"SplitMode=Random");
  factory->BookMethod(dataloader, TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=400:MinNodeSize=4%:MaxDepth=5:BoostType=AdaBoost:AdaBoostBeta=0.15:nCuts=80");
  factory->BookMethod(dataloader, TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher");
  factory->TrainAllMethods(); // Train MVAs using training events
  factory->TestAllMethods(); // Evaluate all MVAs using test events
  // ----- Evaluate and compare performance of all configured MVAs
  factory->EvaluateAllMethods();
  outputFile->Close();
  delete factory; delete dataloader;
  TMVA::TMVAGui("output.root");
}