class TMVA::DecisionTree: public TMVA::BinaryTree

 default constructor using the GiniIndex as separation criterion,
 no restrictions on minium number of events in a leave note or the
 separation gain in the node splitting

 constructor specifying the separation type, the min number of
 events in a no that is still subjected to further splitting, the
 number of bins in the grid used in applying the cut for the node
 splitting.

 copy constructor that creates a true copy, i.e. a completely independent tree
 the node copy will recursively copy all the nodes

 destructor

 descend a tree to find all its leaf nodes, fill max depth reached in the
 tree at the same time.

 re-create a new tree (decision tree or search tree) from XML

 building the decision tree by recursively calling the splitting of
 one (root-) node into two daughter nodes (returns the number of nodes)

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

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

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

 remove those last splits that result in two leaf nodes that
 are both of the type (i.e. both signal or both background)
 this of course is only a reasonable thing to do when you use
 "YesOrNo" leafs, while it might loose s.th. if you use the
 purity information in the nodes.
 --> hence I don't call it automatically in the tree building

 prune (get rid of internal nodes) the Decision tree to avoid overtraining
 serveral different pruning methods can be applied as selected by the
 variable "fPruneMethod".

 run the validation sample through the (pruned) tree and fill in the nodes
 the variables NSValidation and NBValidadtion (i.e. how many of the Signal
 and Background events from the validation sample. This is then later used
 when asking for the "tree quality" ..

 return the misclassification rate of a pruned tree
 a "pruned tree" may have set the variable "IsTerminal" to "arbitrary" at
 any node, hence this tree quality testing will stop there, hence test
 the pruned tree (while the full tree is still in place for normal/later use)

 pass a single validation event throught a pruned decision tree
 on the way down the tree, fill in all the "intermediate" information
 that would normally be there from training.

 calculate the normalization factor for a pruning validation sample

 return the number of terminal nodes in the sub-tree below Node n

 descend a tree to find all its leaf nodes

 prune away the subtree below the node

 prune a node temporaily (without actually deleting its decendants
 which allows testing the pruned tree quality for many different
 pruning stages without "touching" the tree.

 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"  (i.e. 0:left ; 1:right

 Decide how to split a node using one of the variables that gives
 the best separation of signal/background. In order to do this, for each
 variable a scan of the different cut values in a grid (grid = fNCuts) is
 performed and the resulting separation gains are compared.
 in addition to the individual variables, one can also ask for a fisher
 discriminant being built out of (some) of the variables and used as a
 possible multivariate split.

 calculate the fisher coefficients for the event sample and the variables used

 get the pointer to the leaf node where a particular event ends up in...
 (used in gradient boosting)

 the event e is put into the decision tree (starting at the root node)
 and the output is NodeType (signal) or (background) of the final node (basket)
 in which the given events ends up. I.e. the result of the classification if
 the event for this decision tree.

 calculates the purity S/(S+B) of a given event sample

 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 relative improtance of variable ivar

 Retrieves the address of the root node

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

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