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;
      data->Fill();
   }

   data->ResetBranchAddresses();
   return data;
} 

void MinimalClassification()
{
   TFile * outFile = new TFile("out.root", "RECREATE");

   TTree * treeClass0 = genTree(1000, 0.0, 2.0, 100);
   TTree * treeClass1   = genTree(1000, 1.0, 2.0, 101);
   TTree * treeClass1_2 = genTree(1000, 1.0, 2.0, 102);

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

   TMVA::DataLoader d {"dataset"};

   d.AddVariable(  "x" , 'D' );
   d.AddVariable(  "y" , 'D' );

   d.AddSignalTree    ( treeClass0, 1.0 );
   d.AddBackgroundTree( treeClass1  , 2.0 );
   d.AddBackgroundTree( treeClass1_2, 0.5 );

   // d.PrepareTrainingAndTestTree("", "", "SplitMode=Random");
   d.PrepareTrainingAndTestTree("", "(x<2.0) && (y<2.0)", "SplitMode=Random");

   factory.BookMethod( &d, TMVA::Types::kBDT, "BDT", "");

   factory.TrainAllMethods();
   factory.TestAllMethods();
   factory.EvaluateAllMethods();

   outFile->Close();
}