import ROOT
from ROOT import TFile, TMVA, TCut
Welcome to JupyROOT 6.07/07
To use new interactive features in notebook we have to enable a module called JsMVA. This can be done by using ipython magic: %jsmva.
%jsmva on
First let's start with the classical version of declaration. If you know how to use TMVA in C++ then you can use that version here in python: first we need to pass a string called job name, as second argument we need to pass an opened output TFile (this is optional, if it's present then it will be used to store output histograms) and as third (or second) argument we pass a string which contains all the settings related to Factory (separated with ':' character).
outputFile = TFile( "TMVA.root", 'RECREATE' )
TMVA.Tools.Instance();
factory = TMVA.Factory( "TMVAClassification", outputFile #this is optional
,"!V:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" )
The options string can contain the following options:
Option | Default | Predefined values | Description |
---|---|---|---|
V | False | - | Verbose flag |
Color | True | - | Flag for colored output |
Transformations | "" | - | List of transformations to test. For example with "I;D;P;U;G" string identity, decorrelation, PCA, uniform and Gaussian transformations will be applied |
Silent | False | - | Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object |
DrawProgressBar | True | - | Draw progress bar to display training, testing and evaluation schedule (default: True) |
AnalysisType | Auto | Classification, Regression, Multiclass, Auto | Set the analysis type |
By enabling JsMVA we have new, more readable ways to do the declaration.
Instead of passing options as a long string we can pass them separately as named arguments:
factory = TMVA.Factory("TMVAClassification", outputFile,
V=False, Color=True,Silent=True, DrawProgressBar=True, Transformations="I;D;P;G,D", AnalysisType="Classification")
You can see the Transformations variable is set to "I;D;P;G;D" string. Instead of this, we can pass these options as a list: ["I", "D", "P", "G", "D"]
In the first version we just changed the way as we pass the options. The first 2 argument was still positional arguments. These parameters also can be passed as named arguments: the name of first parameter in first version is JobName and the name of second argument is TargetFile
factory = TMVA.Factory(JobName="TMVAClassification", TargetFile=outputFile,
V=False, Color=True, DrawProgressBar=True, Transformations=["I", "D", "P", "G", "D"],
AnalysisType="Classification")
Arguments of constructor: The options string can contain the following options:
Keyword | Can be used as positional argument | Default | Predefined values | Description |
---|---|---|---|---|
JobName | yes, 1. | not optional | - | Name of job |
TargetFile | yes, 2. | if not passed histograms won't be saved | - | File to write control and performance histograms histograms |
V | no | False | - | Verbose flag |
Color | no | True | - | Flag for colored output |
Transformations | no | "" | - | List of transformations to test. For example with "I;D;P;U;G" string identity, decorrelation, PCA, uniform and Gaussian transformations will be applied |
Silent | no | False | - | Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object |
DrawProgressBar | no | True | - | Draw progress bar to display training, testing and evaluation schedule (default: True) |
AnalysisType | no | Auto | Classification, Regression, Multiclass, Auto | Set the analysis type |
First we need to declare a DataLoader and add the variables (passing the variable names used in the test and train trees in input dataset). To add variable names to DataLoader we use the AddVariable function. Arguments of this function:
String containing the variable name. Using ":=" we can add definition too.
String (label to variable, if not present the variable name will be used) or character (defining the type of data points)
If we have label for variable, the data point type still can be passed as third argument
dataset = "tmva_class_example" #the dataset name
loader = TMVA.DataLoader(dataset)
loader.AddVariable( "myvar1 := var1+var2", 'F' )
loader.AddVariable( "myvar2 := var1-var2", "Expression 2", 'F' )
loader.AddVariable( "var3", "Variable 3", 'F' )
loader.AddVariable( "var4", "Variable 4", 'F' )
It is possible to define spectator variables, which are part of the input data set, but which are not used in the MVA training, test nor during the evaluation, but can be used for correlation tests or others. Parameters:
loader.AddSpectator( "spec1:=var1*2", "Spectator 1", 'F' )
loader.AddSpectator( "spec2:=var1*3", "Spectator 2", 'F' )
After adding the variables we have to add the datas to DataLoader. In order to do this we check if the dataset file doesn't exist in files directory we download from CERN's server. When we have the root file we open it and get the signal and background trees.
if ROOT.gSystem.AccessPathName( "tmva_class_example.root" ) != 0:
ROOT.gSystem.Exec( "wget https://root.cern.ch/files/tmva_class_example.root")
input = TFile.Open( "tmva_class_example.root" )
# Get the signal and background trees for training
signal = input.Get( "TreeS" )
background = input.Get( "TreeB" )
To pass the signal and background trees to DataLoader we use the AddSignalTree and AddBackgroundTree functions, and we set up the corresponding DataLoader variable's too. Arguments of functions:
# Global event weights (see below for setting event-wise weights)
signalWeight = 1.0
backgroundWeight = 1.0
loader.AddSignalTree(signal, signalWeight)
loader.AddBackgroundTree(background, backgroundWeight)
loader.fSignalWeight = signalWeight
loader.fBackgroundWeight = backgroundWeight
loader.fTreeS = signal
loader.fTreeB = background
With using DataLoader.PrepareTrainingAndTestTree function we apply cuts on input events. In C++ this function also needs to add the options as a string (as we seen in Factory constructor) which with JsMVA can be passed (same as Factory constructor case) as keyword arguments.
Arguments of PrepareTrainingAndTestTree:
Keyword | Can be used as positional argument | Default | Predefined values | Description |
---|---|---|---|---|
SigCut | yes, 1. | - | - | TCut object for signal cut |
Bkg | yes, 2. | - | - | TCut object for background cut |
SplitMode | no | Random | Random, Alternate, Block | Method of picking training and testing events |
MixMode | no | SameAsSplitMode | SameAsSplitMode, Random, Alternate, Block | Method of mixing events of differnt classes into one dataset |
SplitSeed | no | 100 | - | Seed for random event shuffling |
NormMode | no | EqualNumEvents | None, NumEvents, EqualNumEvents | Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal) |
nTrain_Signal | no | 0 (all) | - | Number of training events of class Signal |
nTest_Signal | no | 0 (all) | - | Number of test events of class Signal |
nTrain_Background | no | 0 (all) | - | Number of training events of class Background |
nTest_Background | no | 0 (all) | - | Number of test events of class Background |
V | no | False | - | Verbosity |
VerboseLevel | no | Info | Debug, Verbose, Info | Verbosity level |
mycuts = TCut("")
mycutb = TCut("")
loader.PrepareTrainingAndTestTree(SigCut=mycuts, BkgCut=mycutb,
nTrain_Signal=0, nTrain_Background=0, SplitMode="Random", NormMode="NumEvents", V=False)
To add which we want to train on dataset we have to use the Factory.BookMethod function. This method will add a method and it's options to Factory.
Arguments:
Keyword | Can be used as positional argument | Default | Predefined values | Description |
---|---|---|---|---|
DataLoader | yes, 1. | - | - | Pointer to DataLoader object |
Method | yes, 2. | - | kVariable kCuts , kLikelihood , kPDERS , kHMatrix , kFisher , kKNN , kCFMlpANN , kTMlpANN , kBDT , kDT , kRuleFit , kSVM , kMLP , kBayesClassifier, kFDA , kBoost , kPDEFoam , kLD , kPlugins , kCategory , kDNN , kPyRandomForest , kPyAdaBoost , kPyGTB , kC50 , kRSNNS , kRSVM , kRXGB , kMaxMethod | Selected method number, method numbers defined in TMVA.Types |
MethodTitle | yes, 3. | - | - | Label for method |
* | no | - | - | Other named arguments which are the options for selected method. |
factory.BookMethod( DataLoader=loader, Method=TMVA.Types.kMLP, MethodTitle="MLP",
H=False, V=False, NeuronType="tanh", VarTransform="N", NCycles=600, HiddenLayers="N+5",
TestRate=5, UseRegulator=False )
<ROOT.TMVA::MethodMLP object ("MLP") at 0x4dbd0b0>
To calculate variable importance we can use Factory.EvaluateImportance function. The parameters of this function are the following:
Keyword | Can be used as positional argument | Default | Predefined values | Description |
---|---|---|---|---|
DataLoader | yes, 1. | - | - | Pointer to DataLoader object |
VIType | yes, 2. | - | - | Variable Importance type |
Method | yes, 3. | - | kVariable kCuts , kLikelihood , kPDERS , kHMatrix , kFisher , kKNN , kCFMlpANN , kTMlpANN , kBDT , kDT , kRuleFit , kSVM , kMLP , kBayesClassifier, kFDA , kBoost , kPDEFoam , kLD , kPlugins , kCategory , kDNN , kPyRandomForest , kPyAdaBoost , kPyGTB , kC50 , kRSNNS , kRSVM , kRXGB , kMaxMethod | Selected method number, method numbers defined in TMVA.Types |
MethodTitle | yes, 4. | - | - | Label for method |
V | no | False | - | Verbose |
NTrees | no | NTrees | ||
MinNodeSize | no | MinNodeSize | ||
MaxDepth | no | MaxDepth | ||
BoostType | no | BoostType | ||
AdaBoostBeta | no | AdaBoostBeta | ||
UseBaggedBoost | no | UseBaggedBoost | ||
BaggedSampleFraction | no | |||
SeparationType | no | |||
nCuts | no | nCuts |
factory.EvaluateImportance(DataLoader=loader,VIType=0, Method=TMVA.Types.kBDT, MethodTitle="BDT",
V=False,NTrees=5, MinNodeSize="2.5%",MaxDepth=2, BoostType="AdaBoost", AdaBoostBeta=0.5,
UseBaggedBoost=True, BaggedSampleFraction=0.5, SeparationType="GiniIndex", nCuts=20 );
Evaluation results ranked by best signal efficiency and purity (area) | |||||||||||||||||||||||||||||||
DataSet MVA | |||||||||||||||||||||||||||||||
Name: Method: ROC-integ | |||||||||||||||||||||||||||||||
00000000000000000000000000001111 BDT : 0.830 | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
00000000000000000000000000001111 BDT : 0.000 (0.000) 0.000 (0.000) 0.866 (0.871) | |||||||||||||||||||||||||||||||
Factory | Evaluate classifier: MLP | ||||||||||||||||||||||||||||||
TFHandler_MLP |
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MLP |
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Evaluation results ranked by best signal efficiency and purity (area) | |||||||||||||||||||||||||||||||
DataSet MVA | |||||||||||||||||||||||||||||||
Name: Method: ROC-integ | |||||||||||||||||||||||||||||||
tmva_class_example MLP : 0.939 | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
tmva_class_example MLP : 0.382 (0.349) 0.802 (0.794) 0.964 (0.966) | |||||||||||||||||||||||||||||||
Factory | Thank you for using TMVA! | ||||||||||||||||||||||||||||||
For citation information, please visit: http://tmva.sf.net/citeTMVA.html | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeS of type Signal with 6000 events | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeB of type Background with 6000 events | |||||||||||||||||||||||||||||||
Factory | Booking method: BDT | ||||||||||||||||||||||||||||||
DataSetFactory |
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DataSetInfo | Correlation matrix (Signal) | ||||||||||||||||||||||||||||||
DataSetInfo | Correlation matrix (Background) | ||||||||||||||||||||||||||||||
DataSetFactory |
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Factory | Train method: BDT for Classification | ||||||||||||||||||||||||||||||
BDT | #events: (reweighted) sig: 3000 bkg: 3000 | ||||||||||||||||||||||||||||||
#events: (unweighted) sig: 3000 bkg: 3000 | |||||||||||||||||||||||||||||||
Training 5 Decision Trees ... patience please | |||||||||||||||||||||||||||||||
Elapsed time for training with 6000 events : 0.018 sec | |||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00555 sec | |||||||||||||||||||||||||||||||
Factory | Training finished | ||||||||||||||||||||||||||||||
Ranking input variables (method specific)... | |||||||||||||||||||||||||||||||
BDT | Ranking result (top variable is best ranked) | ||||||||||||||||||||||||||||||
Rank : Variable : Variable Importance | |||||||||||||||||||||||||||||||
1 : var4 : 7.847e-01 | |||||||||||||||||||||||||||||||
2 : var3 : 2.153e-01 | |||||||||||||||||||||||||||||||
3 : var1-var2 : 0.000e+00 | |||||||||||||||||||||||||||||||
Factory | Test method: BDT for Classification performance | ||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00515 sec | |||||||||||||||||||||||||||||||
Factory | Evaluate classifier: BDT | ||||||||||||||||||||||||||||||
BDT |
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Evaluation results ranked by best signal efficiency and purity (area) | |||||||||||||||||||||||||||||||
DataSet MVA | |||||||||||||||||||||||||||||||
Name: Method: ROC-integ | |||||||||||||||||||||||||||||||
00000000000000000000000000001110 BDT : 0.790 | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
00000000000000000000000000001110 BDT : 0.000 (0.000) 0.000 (0.000) 0.769 (0.786) | |||||||||||||||||||||||||||||||
Factory | Thank you for using TMVA! | ||||||||||||||||||||||||||||||
For citation information, please visit: http://tmva.sf.net/citeTMVA.html | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeS of type Signal with 6000 events | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeB of type Background with 6000 events | |||||||||||||||||||||||||||||||
Factory | Booking method: BDT | ||||||||||||||||||||||||||||||
DataSetFactory |
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DataSetInfo | Correlation matrix (Signal) | ||||||||||||||||||||||||||||||
DataSetInfo | Correlation matrix (Background) | ||||||||||||||||||||||||||||||
DataSetFactory |
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Factory | Train method: BDT for Classification | ||||||||||||||||||||||||||||||
BDT | #events: (reweighted) sig: 3000 bkg: 3000 | ||||||||||||||||||||||||||||||
#events: (unweighted) sig: 3000 bkg: 3000 | |||||||||||||||||||||||||||||||
Training 5 Decision Trees ... patience please | |||||||||||||||||||||||||||||||
Elapsed time for training with 6000 events : 0.0175 sec | |||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00503 sec | |||||||||||||||||||||||||||||||
Factory | Training finished | ||||||||||||||||||||||||||||||
Ranking input variables (method specific)... | |||||||||||||||||||||||||||||||
BDT | Ranking result (top variable is best ranked) | ||||||||||||||||||||||||||||||
Rank : Variable : Variable Importance | |||||||||||||||||||||||||||||||
1 : var4 : 6.324e-01 | |||||||||||||||||||||||||||||||
2 : var1+var2 : 3.662e-01 | |||||||||||||||||||||||||||||||
3 : var3 : 1.458e-03 | |||||||||||||||||||||||||||||||
Factory | Test method: BDT for Classification performance | ||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00549 sec | |||||||||||||||||||||||||||||||
Factory | Evaluate classifier: BDT | ||||||||||||||||||||||||||||||
BDT |
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Evaluation results ranked by best signal efficiency and purity (area) | |||||||||||||||||||||||||||||||
DataSet MVA | |||||||||||||||||||||||||||||||
Name: Method: ROC-integ | |||||||||||||||||||||||||||||||
00000000000000000000000000001101 BDT : 0.830 | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
00000000000000000000000000001101 BDT : 0.000 (0.000) 0.000 (0.000) 0.866 (0.871) | |||||||||||||||||||||||||||||||
Factory | Thank you for using TMVA! | ||||||||||||||||||||||||||||||
For citation information, please visit: http://tmva.sf.net/citeTMVA.html | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeS of type Signal with 6000 events | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeB of type Background with 6000 events | |||||||||||||||||||||||||||||||
Factory | Booking method: BDT | ||||||||||||||||||||||||||||||
DataSetFactory |
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DataSetInfo | Correlation matrix (Signal) | ||||||||||||||||||||||||||||||
DataSetInfo | Correlation matrix (Background) | ||||||||||||||||||||||||||||||
DataSetFactory |
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Factory | Train method: BDT for Classification | ||||||||||||||||||||||||||||||
BDT | #events: (reweighted) sig: 3000 bkg: 3000 | ||||||||||||||||||||||||||||||
#events: (unweighted) sig: 3000 bkg: 3000 | |||||||||||||||||||||||||||||||
Training 5 Decision Trees ... patience please | |||||||||||||||||||||||||||||||
Elapsed time for training with 6000 events : 0.0191 sec | |||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00532 sec | |||||||||||||||||||||||||||||||
Factory | Training finished | ||||||||||||||||||||||||||||||
Ranking input variables (method specific)... | |||||||||||||||||||||||||||||||
BDT | Ranking result (top variable is best ranked) | ||||||||||||||||||||||||||||||
Rank : Variable : Variable Importance | |||||||||||||||||||||||||||||||
1 : var4 : 6.333e-01 | |||||||||||||||||||||||||||||||
2 : var1+var2 : 3.667e-01 | |||||||||||||||||||||||||||||||
3 : var1-var2 : 0.000e+00 | |||||||||||||||||||||||||||||||
Factory | Test method: BDT for Classification performance | ||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00483 sec | |||||||||||||||||||||||||||||||
Factory | Evaluate classifier: BDT | ||||||||||||||||||||||||||||||
BDT |
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Evaluation results ranked by best signal efficiency and purity (area) | |||||||||||||||||||||||||||||||
DataSet MVA | |||||||||||||||||||||||||||||||
Name: Method: ROC-integ | |||||||||||||||||||||||||||||||
00000000000000000000000000001011 BDT : 0.830 | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
00000000000000000000000000001011 BDT : 0.000 (0.000) 0.000 (0.000) 0.866 (0.871) | |||||||||||||||||||||||||||||||
Factory | Thank you for using TMVA! | ||||||||||||||||||||||||||||||
For citation information, please visit: http://tmva.sf.net/citeTMVA.html | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeS of type Signal with 6000 events | |||||||||||||||||||||||||||||||
DataSetInfo |
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Add Tree TreeB of type Background with 6000 events | |||||||||||||||||||||||||||||||
Factory | Booking method: BDT | ||||||||||||||||||||||||||||||
DataSetFactory |
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DataSetInfo | Correlation matrix (Signal) | ||||||||||||||||||||||||||||||
DataSetInfo | Correlation matrix (Background) | ||||||||||||||||||||||||||||||
DataSetFactory |
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Factory | Train method: BDT for Classification | ||||||||||||||||||||||||||||||
BDT | #events: (reweighted) sig: 3000 bkg: 3000 | ||||||||||||||||||||||||||||||
#events: (unweighted) sig: 3000 bkg: 3000 | |||||||||||||||||||||||||||||||
Training 5 Decision Trees ... patience please | |||||||||||||||||||||||||||||||
Elapsed time for training with 6000 events : 0.0205 sec | |||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00517 sec | |||||||||||||||||||||||||||||||
Factory | Training finished | ||||||||||||||||||||||||||||||
Ranking input variables (method specific)... | |||||||||||||||||||||||||||||||
BDT | Ranking result (top variable is best ranked) | ||||||||||||||||||||||||||||||
Rank : Variable : Variable Importance | |||||||||||||||||||||||||||||||
1 : var1+var2 : 6.781e-01 | |||||||||||||||||||||||||||||||
2 : var3 : 3.219e-01 | |||||||||||||||||||||||||||||||
3 : var1-var2 : 0.000e+00 | |||||||||||||||||||||||||||||||
Factory | Test method: BDT for Classification performance | ||||||||||||||||||||||||||||||
BDT |
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Elapsed time for evaluation of 6000 events : 0.00503 sec | |||||||||||||||||||||||||||||||
Factory | Evaluate classifier: BDT | ||||||||||||||||||||||||||||||
BDT |
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Evaluation results ranked by best signal efficiency and purity (area) | |||||||||||||||||||||||||||||||
DataSet MVA | |||||||||||||||||||||||||||||||
Name: Method: ROC-integ | |||||||||||||||||||||||||||||||
00000000000000000000000000000111 BDT : 0.780 | |||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||
00000000000000000000000000000111 BDT : 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) | |||||||||||||||||||||||||||||||
Factory | Thank you for using TMVA! | ||||||||||||||||||||||||||||||
For citation information, please visit: http://tmva.sf.net/citeTMVA.html | |||||||||||||||||||||||||||||||
--- Variable Importance Results (Short) | |||||||||||||||||||||||||||||||
--- var1+var2 = 43.9596 % | |||||||||||||||||||||||||||||||
--- var1-var2 = 0 % | |||||||||||||||||||||||||||||||
--- var3 = 0 % | |||||||||||||||||||||||||||||||
--- var4 = 56.0404 % |