class TMVA::MethodBDT: public TMVA::MethodBase

 the standard constructor for the "boosted decision trees"

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

 BDT can handle classification with 2 classes and regression with one regression-target

 define the options (their key words) that can be set in the option string
 know options:
 nTrees        number of trees in the forest to be created
 BoostType     the boosting type for the trees in the forest (AdaBoost e.t.c..)
                  known: AdaBoost
                         AdaBoostR2 (Adaboost for regression)
                         Bagging
                         GradBoost
 AdaBoostBeta     the boosting parameter, beta, for AdaBoost
 UseRandomisedTrees  choose at each node splitting a random set of variables
 UseNvars         use UseNvars variables in randomised trees
 UseNTrainEvents  number of training events used in randomised (and bagged) trees
 SeparationType   the separation criterion applied in the node splitting
                  known: GiniIndex
                         MisClassificationError
                         CrossEntropy
                         SDivSqrtSPlusB
 nEventsMin:      the minimum number of events in a node (leaf criteria, stop splitting)
 nCuts:           the number of steps in the optimisation of the cut for a node (if < 0, then
                  step size is determined by the events)
 UseYesNoLeaf     decide if the classification is done simply by the node type, or the S/B
                  (from the training) in the leaf node
 NodePurityLimit  the minimum purity to classify a node as a signal node (used in pruning and boosting to determine
                  misclassification error rate)
 UseWeightedTrees use average classification from the trees, or have the individual trees
                  trees in the forest weighted (e.g. log(boostweight) from AdaBoost
 PruneMethod      The Pruning method:
                  known: NoPruning  // switch off pruning completely
                         ExpectedError
                         CostComplexity
 PruneStrength    a parameter to adjust the amount of pruning. Should be large enough such that overtraining is avoided.
 PruneBeforeBoost flag to prune the tree before applying boosting algorithm
 PruningValFraction   number of events to use for optimizing pruning (only if PruneStrength < 0, i.e. automatic pruning)
 NoNegWeightsInTraining  Ignore negative weight events in the training.
 NNodesMax        maximum number of nodes allwed in the tree splitting, then it stops.
 MaxDepth         maximum depth of the decision tree allowed before further splitting is stopped

 the option string is decoded, for available options see "DeclareOptions"

 common initialisation with defaults for the BDT-Method

destructor

 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

 BDT training

 fills fEventSample with fSampleFraction*NEvents random training events

returns MVA value: -1 for background, 1 for signal

Calculate residua for all events;

Calculate the desired response value for each region (line search)

 initialize targets for first tree

 test the tree quality.. in terms of Miscalssification

 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 fraction 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 boot-strapping, re-sampling or whatever you like, in the end it is nothing
 else but applying "random" weights to each event.

 a special boosting only for Regression ...
 maybe I'll implement it later...

 adaption of the AdaBoost to regression problems (see H.Drucker 1997)

 and save the weights

 write weights to XML

 reads the BDT from the xml file

 read the weights (BDT coefficients)

 Return the MVA value (range [-1;1]) that classifies the
 event according to the majority vote from the total number of
 decision trees.

 get the regression value generated by the BDTs

 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)

 Returns the measure for the variable importance of variable "ivar"
 which is later used in GetVariableImportance() to calculate the
 relative variable importances.

 Compute ranking of input variables

 Get help message text

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 make ROOT-independent C++ class for classifier response (classifier-specific implementation)

 specific class header

 recursively descends a tree and writes the node instance to the output streem