class TMVA::Factory: public TMVA::Configurable

 standard constructor
   jobname       : this name will appear in all weight file names produced by the MVAs
   theTargetFile : output ROOT file; the test tree and all evaluation plots
                   will be stored here
   theOption     : option string; currently: "V" for verbose

 print welcome message
 options are: kLogoWelcomeMsg, kIsometricWelcomeMsg, kLeanWelcomeMsg

 destructor
   delete fATreeEvent;

 delete methods

 create the data assignment tree (for event-wise data assignment by user)

 add signal training event

 add signal testing event

 add signal training event

 add signal training event

 add signal training event

 add signal test event

 add event
 vector event : the order of values is: variables + targets + spectators

 assign event-wise local trees to data set

 number of signal events (used to compute significance)

 number of signal events (used to compute significance)

 add signal tree from text file

 number of signal events (used to compute significance)

 add background tree from text file

 set background tree

 define the input trees for signal and background; no cuts are applied

 define the input trees for signal and background from single input tree,
 containing both signal and background events distinguished by the type
 identifiers: SigCut and BgCut

 user inserts discriminating variable in data set info

 user inserts discriminating variable in data set info

 user inserts target in data set info

 user inserts target in data set info

 default creation

 fill input variables in data set

Log() << kWarning << DefaultDataSetInfo().GetNClasses() /*fClasses.size()*/ << Endl;

 prepare the training and test trees

 prepare the training and test trees
 kept for backward compatibility

 prepare the training and test trees
 -> same cuts for signal and background

 prepare the training and test trees

 Book a classifier or regression method

 books MVA method; the option configuration string is custom for each MVA
 the TString field "theNameAppendix" serves to define (and distinguish)
 several instances of a given MVA, eg, when one wants to compare the
 performance of various configurations

 returns pointer to MVA that corresponds to given method title

 put correlations of input data and a few (default + user
 selected) transformations into the root file

 iterates through all booked methods and sees if they use parameter tuning and if so..
 does just that  i.e. calls "Method::Train()" for different parameter setttings and
 keeps in mind the "optimal one"... and that's the one that will later on be used
 in the main training loop.

 iterates through all booked methods and calls training

 Print predefined help message of classifier
 iterate over methods and test

 Print predefined help message of classifier
 iterate over methods and test

 iterates over all MVA input varables and evaluates them

 iterates over all MVAs that have been booked, and calls their evaluation methods

 special case: regression