TMVA Configuration Options Reference Reference version: TMVA-v4.2.0 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 UseBaggedBoost No False − Use only a random subsample of all events for growing the trees in each iteration. 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. 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 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 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_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)

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