class TMVA::RuleFit

 constructor

 default constructor

 destructor

 init effective number of events (using event weights)

 initialize pointers

 initialize the parameters of the RuleFit method and make rules

 set MethodBase

 calculate the sum of weights

 set the current message type to that of mlog for this class and all other subtools

 build the decision tree using fNTreeSample events from fTrainingEventsRndm

 make a forest of decisiontrees

 save event weights - must be done before making the forest

 save event weights - must be done before making the forest

 Boost the events. The algorithm below is the called AdaBoost.
 See MethodBDT for details.
 Actually, this is a more or less copy of MethodBDT::AdaBoost().

 summary of statistics of all trees
 * end-nodes: average and spread

 Fit the coefficients for the rule ensemble

 calculates the importance of each rule

 evaluate single event

 set the training events randomly

 draw a random subsample of the training events without replacement

 normalize rule importance hists

 if all weights are positive, the scale will be 1/maxweight
 if minimum weight < 0, then the scale will be 1/max(maxweight,abs(minweight))

 Fill cut

 fill lin

 fill rule correlation between vx and vy, weighted with either the importance or the coefficient

 help routine to MakeVisHists() - fills for all variables
   if (rule==0) return;

 help routine to MakeVisHists() - fills for all correlation plots

 get first and second variables from title

 this will create histograms visualizing the rule ensemble

 this will create a histograms intended rather for debugging or for the curious user

 main constructor

 set usage of linear term

 set usage of linear term

 set minimum importance allowed

 set minimum rule distance - see RuleEnsemble

 set path related parameters

 make visualization histograms

 accessors

      const Event*  GetTrainingEvent(UInt_t i, UInt_t isub)  const { return &(fTrainingEvents[fSubsampleEvents[isub]])[i]; }