TMVA::Tools::Instance();

auto inputFile = TFile::Open("https://raw.githubusercontent.com/iml-wg/tmvatutorials/master/inputdata.root");
auto outputFile = TFile::Open("TMVAOutputBDT.root", "RECREATE");

TMVA::Factory factory("TMVARegression", outputFile,
                      "!V:!Silent:Color:DrawProgressBar:AnalysisType=Regression" ); 

TMVA::DataLoader loader("dataset"); 

// Add the feature variables, names reference branches in inputFile ttree
loader.AddVariable("var1");
loader.AddVariable("var2");
loader.AddVariable("var3");
loader.AddVariable("var4");
loader.AddVariable("var5 := var1-var3"); // create new features
loader.AddVariable("var6 := var1+var2");

loader.AddTarget( "target := var2+var3" ); // define the target for the regression


TTree *tree;
inputFile->GetObject("Sig", tree);

TCut mycuts = ""; // e.g. TCut mycuts = "abs(var1)<0.5";

loader.AddRegressionTree(tree, 1.0);   // link the TTree to the loader, weight for each event  = 1
loader.PrepareTrainingAndTestTree(mycuts,
                                   "nTrain_Regression=1000:nTest_Regression=1000:SplitMode=Random:NormMode=NumEvents:!V" );

// Boosted Decision Trees 
factory.BookMethod(&loader,TMVA::Types::kBDT, "BDTG",
                   TString("!H:!V:NTrees=64::BoostType=Grad:Shrinkage=0.3:nCuts=20:MaxDepth=4:")+
                   TString("RegressionLossFunctionBDTG=AbsoluteDeviation"));

factory.TrainAllMethods();

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

%jsroot on
outputFile->Close();
auto resultsFile = TFile::Open("TMVAOutputBDT.root");
auto resultsTree = resultsFile->Get("dataset/TestTree"); 
TCanvas c;
resultsTree->Draw("BDTG-target"); // BDTG is the predicted value, target is the true value
c.Draw();