==> Start TMVAClassificationApplication
                         : Booking "BDT method" of type "BDT" from dataset/weights/TMVAClassification_BDT.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_BDT.weights.xml
<HEADER> DataSetInfo              : [Default] : Added class "Signal"
<HEADER> DataSetInfo              : [Default] : Added class "Background"
                         : Booked classifier "BDT" of type: "BDT"
                         : Booking "Cuts method" of type "Cuts" from dataset/weights/TMVAClassification_Cuts.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_Cuts.weights.xml
                         : Read cuts optimised using sample of MC events
                         : Reading 100 signal efficiency bins for 4 variables
                         : Booked classifier "Cuts" of type: "Cuts"
                         : Booking "CutsD method" of type "Cuts" from dataset/weights/TMVAClassification_CutsD.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_CutsD.weights.xml
                         : Read cuts optimised using sample of MC events
                         : Reading 100 signal efficiency bins for 4 variables
                         : Booked classifier "CutsD" of type: "Cuts"
                         : Booking "FDA_GA method" of type "FDA" from dataset/weights/TMVAClassification_FDA_GA.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_FDA_GA.weights.xml
                         : User-defined formula string       : "(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3"
                         : TFormula-compatible formula string: "[0]+[1]*[5]+[2]*[6]+[3]*[7]+[4]*[8]"
                         : Booked classifier "FDA_GA" of type: "FDA"
                         : Booking "KNN method" of type "KNN" from dataset/weights/TMVAClassification_KNN.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_KNN.weights.xml
                         : Creating kd-tree with 2000 events
                         : Computing scale factor for 1d distributions: (ifrac, bottom, top) = (80%, 10%, 90%)
<HEADER> ModulekNN                : Optimizing tree for 4 variables with 2000 values
                         : <Fill> Class 1 has     1000 events
                         : <Fill> Class 2 has     1000 events
                         : Booked classifier "KNN" of type: "KNN"
                         : Booking "LD method" of type "LD" from dataset/weights/TMVAClassification_LD.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_LD.weights.xml
                         : Booked classifier "LD" of type: "LD"
                         : Booking "Likelihood method" of type "Likelihood" from dataset/weights/TMVAClassification_Likelihood.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_Likelihood.weights.xml
                         : Booked classifier "Likelihood" of type: "Likelihood"
                         : Booking "LikelihoodPCA method" of type "Likelihood" from dataset/weights/TMVAClassification_LikelihoodPCA.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_LikelihoodPCA.weights.xml
                         : Booked classifier "LikelihoodPCA" of type: "Likelihood"
                         : Booking "MLPBNN method" of type "MLP" from dataset/weights/TMVAClassification_MLPBNN.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_MLPBNN.weights.xml
<HEADER> MLPBNN                   : Building Network. 
                         : Initializing weights
                         : Booked classifier "MLPBNN" of type: "MLP"
                         : Booking "PDEFoam method" of type "PDEFoam" from dataset/weights/TMVAClassification_PDEFoam.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_PDEFoam.weights.xml
                         : Read foams from file: dataset/weights/TMVAClassification_PDEFoam.weights_foams.root
                         : Booked classifier "PDEFoam" of type: "PDEFoam"
                         : Booking "PDERS method" of type "PDERS" from dataset/weights/TMVAClassification_PDERS.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_PDERS.weights.xml
                         : signal and background scales: 0.001 0.001
                         : Booked classifier "PDERS" of type: "PDERS"
                         : Booking "RuleFit method" of type "RuleFit" from dataset/weights/TMVAClassification_RuleFit.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_RuleFit.weights.xml
                         : Booked classifier "RuleFit" of type: "RuleFit"
                         : Booking "SVM method" of type "SVM" from dataset/weights/TMVAClassification_SVM.weights.xml.
                         : Reading weight file: dataset/weights/TMVAClassification_SVM.weights.xml
                         : Booked classifier "SVM" of type: "SVM"
--- TMVAClassificationApp    : Using input file: /github/home/ROOT-CI/build/tutorials/machine_learning/data/tmva_class_example.root
--- Select signal sample
                         : Rebuilding Dataset Default
--- End of event loop: Real time 0:00:00, CP time 0.660
--- Created root file: "TMVApp.root" containing the MVA output histograms
==> TMVAClassificationApplication is done!