Trainable decision tree classifier
W = TREEC(A,CRIT,PRUNE,T)
W = A*TREEC(,CRIT,PRUNE,T)
W = A*TREEC(CRIT,PRUNE,T)
Computation of a decision tree classifier out of a dataset A using a binary splitting criterion CRIT
INFCRIT - information gain MAXCRIT - purity (default) FISHCRIT - Fisher criterion
Pruning is defined by prune
PRUNE = -1 pessimistic pruning as defined by Quinlan. PRUNE = -2 testset pruning using the dataset T, or, if not supplied, an artificially generated testset of 5 x size of the training set based on parzen density estimates. see PARZENML and GENDATP. PRUNE = 0 no pruning (default). PRUNE > 0 early pruning, e.g. prune = 3 PRUNE = 10 causes heavy pruning.
If CRIT or PRUNE are set to NaN they are optimised by REGOPTC.
 L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and regression trees, Wadsworth, California, 1984.
datasets, mappings, tree_map, regoptc,
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