DataSetInfo : [dataset] : Added class "Signal" : Add Tree of type Signal with 1000 events

DataSetInfo : [dataset] : Added class "Background" : Add Tree of type Background with 1000 events

Factory : Booking method: BDT : : Rebuilding Dataset dataset : Building event vectors for type 2 Signal : Dataset[dataset] : create input formulas for tree : Building event vectors for type 2 Background : Dataset[dataset] : create input formulas for tree

DataSetFactory : [dataset] : Number of events in input trees : : : Dataset[dataset] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ... : Dataset[dataset] : such that the effective (weighted) number of events in each class is the same : Dataset[dataset] : (and equals the number of events (entries) given for class=0 ) : Dataset[dataset] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ... : Dataset[dataset] : ... (note that N_j is the sum of TRAINING events : Dataset[dataset] : ..... Testing events are not renormalised nor included in the renormalisation factor!) : Number of training and testing events : --------------------------------------------------------------------------- : Signal -- training events : 500 : Signal -- testing events : 500 : Signal -- training and testing events: 1000 : Background -- training events : 500 : Background -- testing events : 500 : Background -- training and testing events: 1000 :

DataSetInfo : Correlation matrix (Signal): : ------------------------ : x y : x: +1.000 -0.034 : y: -0.034 +1.000 : ------------------------

DataSetInfo : Correlation matrix (Background): : ------------------------ : x y : x: +1.000 -0.057 : y: -0.057 +1.000 : ------------------------

DataSetFactory : [dataset] : :

Factory : Train all methods

Factory : [dataset] : Create Transformation "I" with events from all classes. :

: Transformation, Variable selection : : Input : variable 'x' <---> Output : variable 'x' : Input : variable 'y' <---> Output : variable 'y'

TFHandler_Factory : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : x: 1.0188 0.56914 [ 0.00044777 1.9995 ] : y: 1.5175 0.74452 [ 0.0054384 2.9981 ] : ----------------------------------------------------------- : Ranking input variables (method unspecific)...

IdTransformation : Ranking result (top variable is best ranked) : -------------------------- : Rank : Variable : Separation : -------------------------- : 1 : y : 5.193e-01 : 2 : x : 5.434e-02 : --------------------------

Factory : Train method: BDT for Classification :

BDT : #events: (reweighted) sig: 500 bkg: 500 : #events: (unweighted) sig: 500 bkg: 500 : Training 800 Decision Trees ... patience please : Elapsed time for training with 1000 events: 0.187 sec

BDT : [dataset] : Evaluation of BDT on training sample (1000 events)

BDT : [dataset] : Evaluation of BDT on training sample (1000 events) : Elapsed time for evaluation of 1000 events: 0.0253 sec : Elapsed time for evaluation of 1000 events: 0.0255 sec : Creating xml weight file: dataset/weights/_BDT.weights.xml : Creating standalone class: dataset/weights/_BDT.class.C : out.root:/dataset/Method_BDT/BDT

Factory : Training finished : : Ranking input variables (method specific)...

BDT : Ranking result (top variable is best ranked) : ----------------------------------- : Rank : Variable : Variable Importance : ----------------------------------- : 1 : x : 5.205e-01 : 2 : y : 4.795e-01 : -----------------------------------

Factory : === Destroy and recreate all methods via weight files for testing === : : Reading weight file: dataset/weights/_BDT.weights.xml

Factory : Test all methods

Factory : Test method: BDT for Classification performance :

BDT : [dataset] : Evaluation of BDT on testing sample (1000 events)

BDT : [dataset] : Evaluation of BDT on testing sample (1000 events) : Elapsed time for evaluation of 1000 events: 0.0224 sec : Elapsed time for evaluation of 1000 events: 0.0226 sec

Factory : Evaluate all methods

Factory : Evaluate classifier: BDT :

BDT : [dataset] : Loop over test events and fill histograms with classifier response... :

TFHandler_BDT : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : x: 1.0177 0.58666 [ 0.0011208 1.9999 ] : y: 1.4705 0.77233 [ 0.024000 2.9933 ] : ----------------------------------------------------------- : : Evaluation results ranked by best signal efficiency and purity (area) : ------------------------------------------------------------------------------------------------------------------- : DataSet MVA : Name: Method: ROC-integ : dataset BDT : 0.875 : ------------------------------------------------------------------------------------------------------------------- : : Testing efficiency compared to training efficiency (overtraining check) : ------------------------------------------------------------------------------------------------------------------- : DataSet MVA Signal efficiency: from test sample (from training sample) : Name: Method: @B=0.01 @B=0.10 @B=0.30 : ------------------------------------------------------------------------------------------------------------------- : dataset BDT : 0.485 (0.705) 0.609 (0.797) 0.794 (0.924) : ------------------------------------------------------------------------------------------------------------------- :

Dataset:dataset : Created tree 'TestTree' with 1000 events :

Dataset:dataset : Created tree 'TrainTree' with 1000 events :

Factory : Thank you for using TMVA! : For citation information, please visit: http://tmva.sf.net/citeTMVA.html