class TMVA::MethodRuleFit: public TMVA::MethodBase

 standard constructor

 constructor from weight file

 destructor

 RuleFit can handle classification with 2 classes

 define the options (their key words) that can be set in the option string
 know options.

 general

 RuleFitModule  <string>
    available values are:    RFTMVA      - use TMVA implementation
                             RFFriedman  - use Friedmans original implementation

 Path search (fitting)

 GDTau          <float>      gradient-directed path: fit threshhold, default
 GDTauPrec      <float>      gradient-directed path: precision of estimated tau
 GDStep         <float>      gradient-directed path: step size
 GDNSteps       <float>      gradient-directed path: number of steps
 GDErrScale     <float>      stop scan when error>scale*errmin

 Tree generation

 fEventsMin     <float>      minimum fraction of events in a splittable node
 fEventsMax     <float>      maximum fraction of events in a splittable node
 nTrees         <float>      number of trees in forest.
 ForestType     <string>
    available values are:    Random    - create forest using random subsample and only random variables subset at each node
                             AdaBoost  - create forest with boosted events


 Model creation

 RuleMinDist    <float>      min distance allowed between rules
 MinImp         <float>      minimum rule importance accepted
 Model          <string>     model to be used
    available values are:    ModRuleLinear <default>
                             ModRule
                             ModLinear


 Friedmans module

 RFWorkDir      <string>     directory where Friedmans module (rf_go.exe) is installed
 RFNrules       <int>        maximum number of rules allowed
 RFNendnodes    <int>        average number of end nodes in the forest of trees

 process the options specified by the user

 initialize the monitoring ntuple

 default initialization

 write all Events from the Tree into a vector of Events, that are
 more easily manipulated.
 This method should never be called without existing trainingTree, as it
 the vector of events from the ROOT training tree

 training of rules using TMVA implementation

 training of rules using Jerome Friedmans implementation

 computes ranking of input variables

 add the rules to XML node

 read rules from an std::istream

 read rules from XML node

 returns MVA value for given event

 write special monitoring histograms to file (here ntuple)

 write specific classifier response

 print out the rule cuts

 print out the linear terms

 get help message text

 typical length of text line:
         "|--------------------------------------------------------------|"

 accessors