DataSetInfo : [dataset] : Added class "Signal"
: Add Tree TreeS of type Signal with 6000 events
DataSetInfo : [dataset] : Added class "Background"
: Add Tree TreeB of type Background with 6000 events
<HEADER> : Loading booked method: BDT BDTG
:
: the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad
: --> change to new default NegWeightTreatment=Pray
: Rebuilding Dataset dataset
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree TreeS
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree TreeB
<HEADER> DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 1000
: Signal -- testing events : 5000
: Signal -- training and testing events: 6000
: Background -- training events : 1000
: Background -- testing events : 5000
: Background -- training and testing events: 6000
:
<HEADER> DataSetInfo : Correlation matrix (Signal):
: ----------------------------------------
: myvar1 myvar2 var3 var4
: myvar1: +1.000 -0.007 +0.754 +0.922
: myvar2: -0.007 +1.000 -0.065 +0.083
: var3: +0.754 -0.065 +1.000 +0.836
: var4: +0.922 +0.083 +0.836 +1.000
: ----------------------------------------
<HEADER> DataSetInfo : Correlation matrix (Background):
: ----------------------------------------
: myvar1 myvar2 var3 var4
: myvar1: +1.000 -0.073 +0.784 +0.925
: myvar2: -0.073 +1.000 -0.142 +0.019
: var3: +0.784 -0.142 +1.000 +0.844
: var4: +0.925 +0.019 +0.844 +1.000
: ----------------------------------------
<HEADER> DataSetFactory : [dataset] :
:
<HEADER> : Loading booked method: SVM SVM
:
<HEADER> SVM : [dataset] : Create Transformation "Norm" with events from all classes.
:
<HEADER> : Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
<HEADER> : Loading booked method: BDT BDTB
:
<HEADER> : Loading booked method: Cuts Cuts
:
: Use optimization method: "Monte Carlo"
: Use efficiency computation method: "Event Selection"
: Use "FSmart" cuts for variable: 'myvar1'
: Use "FSmart" cuts for variable: 'myvar2'
: Use "FSmart" cuts for variable: 'var3'
: Use "FSmart" cuts for variable: 'var4'
: --------------------------------------------------- :
: DataSet MVA :
: Name: Method/Title: ROC-integ :
: --------------------------------------------------- :
: dataset SVM/SVM 0.901 :
: dataset Cuts/Cuts 0.791 :
: dataset BDT/BDTG 0.886 :
: dataset BDT/BDTB 0.854 :
: --------------------------------------------------- :
: -----------------------------------------------------
<HEADER> : Evaluation done.
:
: Jobs = 4 Real Time = 2.005162
: -----------------------------------------------------
: Evaluation done.
