\begin{appendix}

\section{More Classifier Booking Examples}

\label{sec:appendix:booking}

The Code Examples~\ref{codeex:factoryBookingAll1} and \ref{codeex:factoryBookingAll2}

give a (non-exhaustive) collection of classifier bookings with appropriate

default options. They correspond to the example training job \code{TMVAClassification.C}.

\begin{codeexample}

\begin{tmvacode}

// Cut optimisation using Monte Carlo sampling

factory->BookMethod( TMVA::Types::kCuts, "Cuts",

"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );

// Cut optmisation using Genetic Algorithm

factory->BookMethod( TMVA::Types::kCuts, "CutsGA",

"H:!V:FitMethod=GA:CutRangeMin=-10:CutRangeMax=10:VarProp[1]=FMax:EffSel:\

Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );

// Cut optmisation using Simulated Annealing algorithm

factory->BookMethod( TMVA::Types::kCuts, "CutsSA",

"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:\

InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );

// Likelihood classification (naive Bayes) with Spline PDF parametrisation

factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",

"H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:\

NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );

// Likelihood with decorrelation of input variables

factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",

"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:\

NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );

// Likelihood with unbinned kernel estimator for PDF parametrisation

factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",

"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:\

KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );

\end{tmvacode}

\caption[.]{\codeexampleCaptionSize Examples for booking MVA methods in TMVA for

application to classification and -- where available -- to regression problems.

The first argument is

a unique type enumerator (the avaliable types can be looked up in \code{src/Types.h}),

the second is a user-defined name (must be unique among all booked classifiers),

and the third a configuration option string that is specific to the

classifier. For options that are not set in the

string default values are used. The syntax of the options should become

clear from the above examples. Individual options are separated by a

':'. Boolean variables can be set either explicitly as

\code{MyBoolVar=True/False}, or just via \code{MyBoolVar/!MyBoolVar}.

All concrete option variables are explained in the tools and classifier sections

of this Users Guide. The list is continued in Code Example~\ref{codeex:factoryBookingAll2}.}

\label{codeex:factoryBookingAll1}

\end{codeexample}

\begin{codeexample}

\begin{tmvacode}

// Probability density estimator range search method (multi-dimensional)

factory->BookMethod( TMVA::Types::kPDERS, "PDERS",

"!H:V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:\

GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );

// Multi-dimensional PDE using self-adapting phase-space binning

factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",

"H:V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:\

nSampl=2000:nBin=5:CutNmin=T:Nmin=100:Kernel=None:Compress=T" );

// k-Nearest Neighbour method (similar to PDE-RS)

factory->BookMethod( TMVA::Types::kKNN, "KNN",

"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:\

UseWeight=T:!Trim" );

// H-matrix (chi-squared) method

factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );

// Fisher discriminant (also creating Rarity distribution of MVA output)

factory->BookMethod( TMVA::Types::kFisher, "Fisher",

"H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=60:\

NsmoothMVAPdf=10" );

// Fisher discriminant with Gauss-transformed input variables

factory->BookMethod( TMVA::Types::kFisher, "FisherG", "VarTransform=Gauss" );

// Fisher discriminant with principle-value-transformed input variables

factory->BookMethod( TMVA::Types::kFisher, "FisherG", "VarTransform=PCA" );

// Boosted Fisher discriminant

factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher",

"Boost_Num=20:Boost_Transform=log:\

Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2");

// Linear discriminant (same as Fisher, but also performing regression)

factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None" );

// Function discrimination analysis (FDA), fitting user-defined function

factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",

"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:\

ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:\

ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );

\end{tmvacode}

\caption[.]{\codeexampleCaptionSize Continuation from Code Example~\ref{codeex:factoryBookingAll1}.

Continued in Code Example~\ref{codeex:factoryBookingAll2}.}

\label{codeex:factoryBookingAll2}

\end{codeexample}

\begin{codeexample}

\begin{tmvacode}

// Artificial Neural Network (Multilayer perceptron) - TMVA version

factory->BookMethod( TMVA::Types::kMLP, "MLP",

"H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:\

TestRate=5" );

// NN with BFGS quadratic minimisation

factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS",

"H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:\

TestRate=5:TrainingMethod=BFGS" );

// NN (Multilayer perceptron) - ROOT version

factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN",

"!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:

ValidationFraction=0.3" );

// NN (Multilayer perceptron) - ALEPH version (depreciated)

factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN",

"!H:!V:NCycles=2000:HiddenLayers=N+1,N" );

// Support Vector Machine

factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001" );

// Boosted Decision Trees with adaptive boosting

factory->BookMethod( TMVA::Types::kBDT, "BDT",

"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:\

SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );

// Boosted Decision Trees with gradient boosting

factory->BookMethod( TMVA::Types::kBDT, "BDTG",

"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:\

GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:\

PruneMethod=CostComplexity:PruneStrength=50:NNodesMax=5" );

// Boosted Decision Trees with bagging

factory->BookMethod( TMVA::Types::kBDT, "BDTB",

"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:\

nCuts=20:PruneMethod=NoPruning" );

// Predictive learning via rule ensembles (RuleFit)

factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",

"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:\

RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:\

GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );

\end{tmvacode}

\caption[.]{\codeexampleCaptionSize Continuation from Code Example~\ref{codeex:factoryBookingAll2}.}

\label{codeex:factoryBookingAll3}

\end{codeexample}

\end{appendix}

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