class TMVA::DecisionTree: public TMVA::BinaryTree

 the constructur needed for the "reading" of the decision tree from weight files

 the constructur needed for constructing the decision tree via training with events

 copy constructor

 Retrieves the address of the root node

 building of a tree by recursivly splitting the nodes
      UInt_t BuildTree( const EventList & eventSample,
                        DecisionTreeNode *node = NULL);

 determine the way how a node is split (which variable, which cut value)

 fill at tree with a given structure already (just see how many signa/bkgr
 events end up in each node

 fill the existing the decision tree structure by filling event
 in from the top node and see where they happen to end up

 returns: 1 = Signal (right),  -1 = Bkg (left)

 return the individual relative variable importance

 clear the tree nodes (their S/N, Nevents etc), just keep the structure of the tree

 recursive pruning of the tree, validation sample required for automatic pruning

 manage the pruning strength parameter (iff < 0 -> automate the pruning process)

 apply pruning validation sample to a decision tree

 return the misclassification rate of a pruned tree

 pass a single validation event throught a pruned decision tree

 calculate the normalization factor for a pruning validation sample

 retrieve node from the tree. Its position (up to a maximal tree depth of 64)
 is coded as a sequence of left-right moves starting from the root, coded as
 0-1 bit patterns stored in the "long-integer" together with the depth

 prune a node from the tree without deleting its descendants; allows one to
 effectively prune a tree many times without making deep copies

 utility functions
 calculate the Purity out of the number of sig and bkg events collected
 from individual samples.
 calculates the purity S/(S+B) of a given event sample