class TMVA::MethodCommittee: public TMVA::MethodBase

 constructor

 constructor for calculating Committee-MVA using previously generatad decision trees
 the result of the previous training (the decision trees) are read in via the
 weightfile. Make sure the "theVariables" correspond to the ones used in
 creating the "weight"-file

 define the options (their key words) that can be set in the option string
 know options:
 NMembers           <string>     number of members in the committee
 UseMemberDecision  <bool>       use signal information from event (otherwise assume signal)
 UseWeightedMembers <bool>       use weighted trees or simple average in classification from the forest

 BoostType          <string>     boosting type
    available values are:        AdaBoost  <default>
                                 Bagging

 process user options

 common initialisation with defaults for the Committee-Method

destructor

 Function to write options and weights to file

 default sanity checks

 apply the boosting alogrithim (the algorithm is selecte via the the "option" given
 in the constructor. The return value is the boosting weight

 the AdaBoost implementation.
 a new training sample is generated by weighting
 events that are misclassified by the decision tree. The weight
 applied is w = (1-err)/err or more general:
            w = ((1-err)/err)^beta
 where err is the fracthin of misclassified events in the tree ( <0.5 assuming
 demanding the that previous selection was better than random guessing)
 and "beta" beeing a free parameter (standard: beta = 1) that modifies the
 boosting.

 call it Bootstrapping, re-sampling or whatever you like, in the end it is nothing
 else but applying "random boostweights" to each event.

 write the state of the method to an output stream

 read the state of the method from an input stream

 return the MVA value (range [-1;1]) that classifies the
 event.according to the majority vote from the total number of
 decision trees
 In the literature I found that people actually use the
 weighted majority vote (using the boost weights) .. However I
 did not see any improvement in doing so :(
 --> this is currently switched off

 here we could write some histograms created during the processing
 to the output file.

 return the relative variable importance, normalized to all
 variables together having the importance 1. The importance in
 evaluated as the total separation-gain that this variable had in
 the decision trees (weighted by the number of events)

 return the variable importance

 computes ranking of input variables

 write specific classifier response

 get help message text

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 accessors

 accessors