TMVA Options Reference

TMVA Configuration Options Reference | Reference version: TMVA-v-unknown | TMVA-version @ ROOT
Reference for configuration options defined in the option string of each MVA method booking, and for the definition of data sets used for training and testing (Factory). Table fields:
| Option: | The option identifier in the option string (given, e.g., in "factory->BookMethod(...)" call).
| Array: | Can the option be set individually for each input variable via the "[i]" tag, where "i" is the ith variable?
| Default value: | Value used if option is not explicitly set in the configuration option string.
| Predefined values: | Options can be categories of predefined values among which the user must choose.
| Description: | Info about the option.
Colour codes:
| Greenish rows: | Options shared by all MVA methods (through common base class).
| Bluish rows: | Specific MVA options.
| Yellowish rows: | Configuration options for minimiser (fitter) classes.
| Redish rows: | Options for other configurable classes.
Available MVA methods (1st row), minimisation tools (2nd row), and other configurables (3rd row):
| [MVA::HMatrix] [MVA::Fisher] [MVA::PDERS] [MVA::FDA] [MVA::LD] [MVA::SVM] [MVA::CFMlpANN] [MVA::KNN] [MVA::BDT] [MVA::Boost] [MVA::RuleFit] [MVA::Likelihood] [MVA::MLP] [MVA::Cuts] [MVA::PDEFoam] [MVA::TMlpANN]
| [Fitter_SA] [Fitter_MC] [Fitter_Minuit] [Fitter_GA]
| [DataSetFactory] [PDF] [Factory]
Configuration options for MVA method : |
Configuration options reference for MVA method: HMatrix
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
Configuration options for MVA method : |
Configuration options reference for MVA method: Fisher
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
Method | No | Fisher | Fisher, Mahalanobis | Discrimination method
Configuration options for MVA method : |
Configuration options reference for MVA method: PDERS
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
VolumeRangeMode | No | Adaptive | Unscaled, MinMax, RMS, Adaptive, kNN | Method to determine volume size
KernelEstimator | No | Box | Box, Sphere, Teepee, Gauss, Sinc3, Sinc5, Sinc7, Sinc9, Sinc11, Lanczos2, Lanczos3, Lanczos5, Lanczos8, Trim | Kernel estimation function
DeltaFrac | No | 3 | − | nEventsMin/Max for minmax and rms volume range
NEventsMin | No | 100 | − | nEventsMin for adaptive volume range
NEventsMax | No | 200 | − | nEventsMax for adaptive volume range
MaxVIterations | No | 150 | − | MaxVIterations for adaptive volume range
InitialScale | No | 0.99 | − | InitialScale for adaptive volume range
GaussSigma | No | 0.1 | − | Width (wrt volume size) of Gaussian kernel estimator
NormTree | No | False | − | Normalize binary search tree
Configuration options for MVA method : |
Configuration options reference for MVA method: FDA
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
Formula | No | (0) | − | The discrimination formula
ParRanges | No | () | − | Parameter ranges
FitMethod | No | MINUIT | MC, GA, SA, MINUIT | Optimisation Method
Converger | No | None | None, MINUIT | FitMethod uses Converger to improve result
Configuration options for MVA method : |
Configuration options reference for MVA method: LD
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
Configuration options for MVA method : |
Configuration options reference for MVA method: SVM
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
Gamma | No | 1 | − | RBF kernel parameter: Gamma (size of the Kernel)
C | No | 1 | − | Cost parameter
Tol | No | 0.01 | − | Tolerance parameter
MaxIter | No | 1000 | − | Maximum number of training loops
Configuration options for MVA method : |
Configuration options reference for MVA method: CFMlpANN
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
NCycles | No | 3000 | − | Number of training cycles
HiddenLayers | No | N,N-1 | − | Specification of hidden layer architecture
Configuration options for MVA method : |
Configuration options reference for MVA method: KNN
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
nkNN | No | 20 | − | Number of k-nearest neighbors
BalanceDepth | No | 6 | − | Binary tree balance depth
ScaleFrac | No | 0.8 | − | Fraction of events used to compute variable width
SigmaFact | No | 1 | − | Scale factor for sigma in Gaussian kernel
Kernel | No | Gaus | − | Use polynomial (=Poln) or Gaussian (=Gaus) kernel
Trim | No | False | − | Use equal number of signal and background events
UseKernel | No | False | − | Use polynomial kernel weight
UseWeight | No | True | − | Use weight to count kNN events
UseLDA | No | False | − | Use local linear discriminant - experimental feature
Configuration options for MVA method : |
Configuration options reference for MVA method: BDT
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
NTrees | No | 800 | − | Number of trees in the forest
MaxDepth | No | 3 | − | Max depth of the decision tree allowed
MinNodeSize | No | 5% | − | Minimum percentage of training events required in a leaf node (default: Classification: 5%, Regression: 0.2%)
nCuts | No | 20 | − | Number of grid points in variable range used in finding optimal cut in node splitting
BoostType | No | AdaBoost | AdaBoost, RealAdaBoost, Bagging, AdaBoostR2, Grad | Boosting type for the trees in the forest
AdaBoostR2Loss | No | Quadratic | Linear, Quadratic, Exponential | Type of Loss function in AdaBoostR2
UseBaggedGrad | No | False | − | Use only a random subsample of all events for growing the trees in each iteration. (Only valid for GradBoost)
Shrinkage | No | 1 | − | Learning rate for GradBoost algorithm
AdaBoostBeta | No | 0.5 | − | Learning rate for AdaBoost algorithm
UseRandomisedTrees | No | False | − | Determine at each node splitting the cut variable only as the best out of a random subset of variables (like in RandomForests)
UseNvars | No | 2 | − | Size of the subset of variables used with RandomisedTree option
UsePoissonNvars | No | True | − | Interpret UseNvars not as fixed number but as mean of a Possion distribution in each split with RandomisedTree option
BaggedSampleFraction | No | 0.6 | − | Relative size of bagged event sample to original size of the data sample (used whenever bagging is used (i.e. UseBaggedGrad, Bagging,)
UseYesNoLeaf | No | True | − | Use Sig or Bkg categories, or the purity=S/(S+B) as classification of the leaf node -> Real-AdaBoost
NegWeightTreatment | No | InverseBoostNegWeights | InverseBoostNegWeights, IgnoreNegWeightsInTraining, PairNegWeightsGlobal, Pray | How to treat events with negative weights in the BDT training (particular the boosting) : IgnoreInTraining; Boost With inverse boostweight; Pair events with negative and positive weights in traning sample and *annihilate* them (experimental!)
NodePurityLimit | No | 0.5 | − | In boosting/pruning, nodes with purity > NodePurityLimit are signal; background otherwise.
SeparationType | No | GiniIndex | CrossEntropy, GiniIndex, GiniIndexWithLaplace, MisClassificationError, SDivSqrtSPlusB, RegressionVariance | Separation criterion for node splitting
DoBoostMonitor | No | False | − | Create control plot with ROC integral vs tree number
UseFisherCuts | No | False | − | Use multivariate splits using the Fisher criterion
MinLinCorrForFisher | No | 0.8 | − | The minimum linear correlation between two variables demanded for use in Fisher criterion in node splitting
UseExclusiveVars | No | False | − | Variables already used in fisher criterion are not anymore analysed individually for node splitting
DoPreselection | No | False | − | and and apply automatic pre-selection for 100% efficient signal (bkg) cuts prior to training
RenormByClass | No | False | − | Individually re-normalize each event class to the original size after boosting
SigToBkgFraction | No | 1 | − | Sig to Bkg ratio used in Training (similar to NodePurityLimit, which cannot be used in real adaboost
PruneMethod | No | NoPruning | NoPruning, ExpectedError, CostComplexity | Note: for BDTs use small trees (e.g.MaxDepth=3) and NoPruning: Pruning: Method used for pruning (removal) of statistically insignificant branches
PruneStrength | No | 0 | − | Pruning strength
PruningValFraction | No | 0.5 | − | Fraction of events to use for optimizing automatic pruning.
nEventsMin | No | 0 | − | deprecated: Use MinNodeSize (in % of training events) instead
GradBaggingFraction | No | 0.6 | − | deprecated: Use *BaggedSampleFraction* instead: Defines the fraction of events to be used in each iteration, e.g. when UseBaggedGrad=kTRUE.
UseNTrainEvents | No | 0 | − | deprecated: Use *BaggedSampleFraction* instead: Number of randomly picked training events used in randomised (and bagged) trees
NNodesMax | No | 0 | − | deprecated: Use MaxDepth instead to limit the tree size
Configuration options for MVA method : |
Configuration options reference for MVA method: Boost
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
Boost_Num | No | 100 | − | Number of times the classifier is boosted
Boost_MonitorMethod | No | True | − | Write monitoring histograms for each boosted classifier
Boost_DetailedMonitoring | No | False | − | Produce histograms for detailed boost-wise monitoring
Boost_Type | No | AdaBoost | AdaBoost, Bagging, HighEdgeGauss, HighEdgeCoPara | Boosting type for the classifiers
Boost_BaggedSampleFraction | No | 0.6 | − | Relative size of bagged event sample to original size of the data sample (used whenever bagging is used)
Boost_MethodWeightType | No | ByError | ByError, Average, ByROC, ByOverlap, LastMethod | How to set the final weight of the boosted classifiers
Boost_RecalculateMVACut | No | True | − | Recalculate the classifier MVA Signallike cut at every boost iteration
Boost_AdaBoostBeta | No | 1 | − | The ADA boost parameter that sets the effect of every boost step on the events' weights
Boost_Transform | No | step | step, linear, log, gauss | Type of transform applied to every boosted method linear, log, step
Boost_RandomSeed | No | 0 | − | Seed for random number generator used for bagging
Configuration options for MVA method : |
Configuration options reference for MVA method: RuleFit
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
GDTau | No | -1 | − | Gradient-directed (GD) path: default fit cut-off
GDTauPrec | No | 0.01 | − | GD path: precision of tau
GDStep | No | 0.01 | − | GD path: step size
GDNSteps | No | 10000 | − | GD path: number of steps
GDErrScale | No | 1.1 | − | Stop scan when error > scale*errmin
LinQuantile | No | 0.025 | − | Quantile of linear terms (removes outliers)
GDPathEveFrac | No | 0.5 | − | Fraction of events used for the path search
GDValidEveFrac | No | 0.5 | − | Fraction of events used for the validation
fEventsMin | No | 0.1 | − | Minimum fraction of events in a splittable node
fEventsMax | No | 0.9 | − | Maximum fraction of events in a splittable node
nTrees | No | 20 | − | Number of trees in forest.
ForestType | No | AdaBoost | AdaBoost, Random | Method to use for forest generation (AdaBoost or RandomForest)
RuleMinDist | No | 0.001 | − | Minimum distance between rules
MinImp | No | 0.01 | − | Minimum rule importance accepted
Model | No | ModRuleLinear | ModRule, ModRuleLinear, ModLinear | Model to be used
RuleFitModule | No | RFTMVA | RFTMVA, RFFriedman | Which RuleFit module to use
RFWorkDir | No | ./rulefit | − | Friedman's RuleFit module (RFF): working dir
RFNrules | No | 2000 | − | RFF: Mximum number of rules
RFNendnodes | No | 4 | − | RFF: Average number of end nodes
Configuration options for MVA method : |
Configuration options reference for MVA method: Likelihood
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
TransformOutput | No | False | − | Transform likelihood output by inverse sigmoid function
Configuration options for MVA method : |
Configuration options reference for MVA method: MLP
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
NCycles | No | 500 | − | Number of training cycles
HiddenLayers | No | N,N-1 | − | Specification of hidden layer architecture
NeuronType | No | sigmoid | − | Neuron activation function type
RandomSeed | No | 1 | − | Random seed for initial synapse weights (0 means unique seed for each run; default value '1')
EstimatorType | No | MSE | MSE, CE, linear, sigmoid, tanh, radial | MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood
NeuronInputType | No | sum | sum, sqsum, abssum | Neuron input function type
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
TrainingMethod | No | BP | BP, GA, BFGS | Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)
LearningRate | No | 0.02 | − | ANN learning rate parameter
DecayRate | No | 0.01 | − | Decay rate for learning parameter
TestRate | No | 10 | − | Test for overtraining performed at each #th epochs
EpochMonitoring | No | False | − | Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)
Sampling | No | 1 | − | Only 'Sampling' (randomly selected) events are trained each epoch
SamplingEpoch | No | 1 | − | Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training
SamplingImportance | No | 1 | − | The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.
SamplingTraining | No | True | − | The training sample is sampled
SamplingTesting | No | False | − | The testing sample is sampled
ResetStep | No | 50 | − | How often BFGS should reset history
Tau | No | 3 | − | LineSearch size step
BPMode | No | sequential | sequential, batch | Back-propagation learning mode: sequential or batch
BatchSize | No | -1 | − | Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events
ConvergenceImprove | No | 1e-30 | − | Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)
ConvergenceTests | No | -1 | − | Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)
UseRegulator | No | False | − | Use regulator to avoid over-training
UpdateLimit | No | 10000 | − | Maximum times of regulator update
CalculateErrors | No | False | − | Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value
WeightRange | No | 1 | − | Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range
Configuration options for MVA method : |
Configuration options reference for MVA method: Cuts
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
FitMethod | No | GA | GA, SA, MC, MCEvents, MINUIT, EventScan | Minimisation Method (GA, SA, and MC are the primary methods to be used; the others have been introduced for testing purposes and are depreciated)
EffMethod | No | EffSel | EffSel, EffPDF | Selection Method
CutRangeMin | Yes | -1 | − | Minimum of allowed cut range (set per variable)
CutRangeMax | Yes | -1 | − | Maximum of allowed cut range (set per variable)
VarProp | Yes | NotEnforced | NotEnforced, FMax, FMin, FSmart | Categorisation of cuts
Configuration options for MVA method : |
Configuration options reference for MVA method: PDEFoam
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
SigBgSeparate | No | False | − | Separate foams for signal and background
TailCut | No | 0.001 | − | Fraction of outlier events that are excluded from the foam in each dimension
VolFrac | No | 0.0666667 | − | Size of sampling box, used for density calculation during foam build-up (maximum value: 1.0 is equivalent to volume of entire foam)
nActiveCells | No | 500 | − | Maximum number of active cells to be created by the foam
nSampl | No | 2000 | − | Number of generated MC events per cell
nBin | No | 5 | − | Number of bins in edge histograms
Compress | No | True | − | Compress foam output file
MultiTargetRegression | No | False | − | Do regression with multiple targets
Nmin | No | 100 | − | Number of events in cell required to split cell
MaxDepth | No | 0 | − | Maximum depth of cell tree (0=unlimited)
FillFoamWithOrigWeights | No | False | − | Fill foam with original or boost weights
UseYesNoCell | No | False | − | Return -1 or 1 for bkg or signal like events
DTLogic | No | None | None, GiniIndex, MisClassificationError, CrossEntropy, GiniIndexWithLaplace, SdivSqrtSplusB | Use decision tree algorithm to split cells
Kernel | No | None | None, Gauss, LinNeighbors | Kernel type used
TargetSelection | No | Mean | Mean, Mpv | Target selection method
Configuration options for MVA method : |
Configuration options reference for MVA method: TMlpANN
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose output (short form of VerbosityLevel below - overrides the latter one)
VerbosityLevel | No | Default | Default, Debug, Verbose, Info, Warning, Error, Fatal | Verbosity level
VarTransform | No | None | − | List of variable transformations performed before training, e.g., D_Background,P_Signal,G,N_AllClasses for: Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)
H | No | False | − | Print method-specific help message
CreateMVAPdfs | No | False | − | Create PDFs for classifier outputs (signal and background)
IgnoreNegWeightsInTraining | No | False | − | Events with negative weights are ignored in the training (but are included for testing and performance evaluation)
NCycles | No | 200 | − | Number of training cycles
HiddenLayers | No | N,N-1 | − | Specification of hidden layer architecture (N stands for number of variables; any integers may also be used)
ValidationFraction | No | 0.5 | − | Fraction of events in training tree used for cross validation
LearningMethod | No | Stochastic | Stochastic, Batch, SteepestDescent, RibierePolak, FletcherReeves, BFGS | Learning method
Configuration options for setup and tuning of specific fitter :
Configuration options reference for fitting method: Simulated Annealing (SA)
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
MaxCalls | No | 100000 | − | Maximum number of minimisation calls
InitialTemp | No | 1e+06 | − | Initial temperature
MinTemp | No | 1e-06 | − | Mimimum temperature
Eps | No | 1e-10 | − | Epsilon
TempScale | No | 1 | − | Temperature scale
AdaptiveSpeed | No | 1 | − | Adaptive speed
TempAdaptiveStep | No | 0.009875 | − | Step made in each generation temperature adaptive
UseDefaultScale | No | False | − | Use default temperature scale for temperature minimisation algorithm
UseDefaultTemp | No | False | − | Use default initial temperature
KernelTemp | No | IncAdaptive | IncAdaptive, DecAdaptive, Sqrt, Log, Sin, Homo, Geo | Temperature minimisation algorithm
Configuration options for setup and tuning of specific fitter :
Configuration options reference for fitting method: Monte Carlo sampling (MC)
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
SampleSize | No | 100000 | − | Number of Monte Carlo events in toy sample
Sigma | No | -1 | − | If > 0: new points are generated according to Gauss around best value and with Sigma in units of interval length
Seed | No | 100 | − | Seed for the random generator (0 takes random seeds)
Configuration options for setup and tuning of specific fitter :
Configuration options reference for fitting method: TMinuit (MT)
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
ErrorLevel | No | 1 | − | TMinuit: error level: 0.5=logL fit, 1=chi-squared fit
PrintLevel | No | -1 | − | TMinuit: output level: -1=least, 0, +1=all garbage
FitStrategy | No | 2 | − | TMinuit: fit strategy: 2=best
PrintWarnings | No | False | − | TMinuit: suppress warnings
UseImprove | No | True | − | TMinuit: use IMPROVE
UseMinos | No | True | − | TMinuit: use MINOS
SetBatch | No | False | − | TMinuit: use batch mode
MaxCalls | No | 1000 | − | TMinuit: approximate maximum number of function calls
Tolerance | No | 0.1 | − | TMinuit: tolerance to the function value at the minimum
Configuration options for setup and tuning of specific fitter :
Configuration options reference for fitting method: Genetic Algorithm (GA)
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
PopSize | No | 300 | − | Population size for GA
Steps | No | 40 | − | Number of steps for convergence
Cycles | No | 3 | − | Independent cycles of GA fitting
SC_steps | No | 10 | − | Spread control, steps
SC_rate | No | 5 | − | Spread control, rate: factor is changed depending on the rate
SC_factor | No | 0.95 | − | Spread control, factor
ConvCrit | No | 0.001 | − | Convergence criteria
SaveBestGen | No | 1 | − | Saves the best n results from each generation. They are included in the last cycle
SaveBestCycle | No | 10 | − | Saves the best n results from each cycle. They are included in the last cycle. The value should be set to at least 1.0
Trim | No | False | − | Trim the population to PopSize after assessing the fitness of each individual
Seed | No | 100 | − | Set seed of random generator (0 gives random seeds)
Configuration options given in the "PrepareForTrainingAndTesting" call; these options define the creation of the data sets used for training and expert validation by TMVA :
Configuration options reference for class: DataSetFactory
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
SplitMode | No | Random | Random, Alternate, Block | Method of picking training and testing events (default: random)
MixMode | No | SameAsSplitMode | SameAsSplitMode, Random, Alternate, Block | Method of mixing events of differnt classes into one dataset (default: SameAsSplitMode)
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 | − | Number of training events of class Signal (default: 0 = all)
nTest_Signal | No | 0 | − | Number of test events of class Signal (default: 0 = all)
nTrain_Background | No | 0 | − | Number of training events of class Background (default: 0 = all)
nTest_Background | No | 0 | − | Number of test events of class Background (default: 0 = all)
V | No | False | − | Verbosity (default: true)
VerboseLevel | No | Info | Debug, Verbose, Info | VerboseLevel (Debug/Verbose/Info)
Configuration options for the PDF class :
Configuration options reference for class: PDF
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
NSmooth | No | 0 | − | Number of smoothing iterations for the input histograms
MinNSmooth | No | -1 | − | Min number of smoothing iterations, for bins with most data
MaxNSmooth | No | -1 | − | Max number of smoothing iterations, for bins with least data
NAvEvtPerBin | No | 50 | − | Average number of events per PDF bin
Nbins | No | 0 | − | Defined number of bins for the histogram from which the PDF is created
CheckHist | No | False | − | Whether or not to check the source histogram of the PDF
PDFInterpol | No | Spline2 | Spline0, Spline1, Spline2, Spline3, Spline5, KDE | Interpolation method for reference histograms (e.g. Spline2 or KDE)
KDEtype | No | Gauss | Gauss | KDE kernel type (1=Gauss)
KDEiter | No | Nonadaptive | Nonadaptive, Adaptive | Number of iterations (1=non-adaptive, 2=adaptive)
KDEFineFactor | No | 1 | − | Fine tuning factor for Adaptive KDE: Factor to multyply the width of the kernel
KDEborder | No | None | None, Renorm, Mirror | Border effects treatment (1=no treatment , 2=kernel renormalization, 3=sample mirroring)
Configuration options for Factory running :
Configuration options reference for class: Factory
Option | Array | Default value | Predefined values | Description
--- | --- | --- | --- | ---
V | No | False | − | Verbose flag
Color | No | True | − | Flag for coloured screen output (default: True, if in batch mode: False)
Transformations | No |  | − | List of transformations to test; formatting example: Transformations=I;D;P;U;G,D, for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations
Silent | No | False | − | Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)
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 (Classification, Regression, Multiclass, Auto) (default: Auto)
Page created on Mon Jul 29 00:06:19 2013 (© TMVA, 2006−2009) |