SDTREE Decision tree classifier P=SDTREE(DATA) P=SDTREE(DATA,THR) P=SDTREE(DATA,options) P=SDTREE(P,ACTIVE) % set the number of active thresholds INPUT DATA Data set THR Number of thresholds to use OUTPUT P Decision tree pipeline OPTIONS 'test',TSDATA Provide separate set for pruning the tree 'trfrac' Fraction of data used for training/validation split (def: 0.8) 'full' Grow full tree without pruning 'levels',N Limit the number of tree levels (default: grow full tree and prune it using error on the validation subset) 'no pruning' Do not prune the tree DESCRIPTION SDTREE implements decision tree training. By default SDTREE splits the available DATA into training and validation parts. Then it builds the tree on the training part and estimates error using the validation subset. Tree with minimal validation set error is returned. SDTREE is scalable to very large numbers of samples. To grow full tree without pruning, use: >> p=sdtree(data,'full') 'full' is synonym for: sdtree(data,'no pruning','minsamples',1,'thr','samples') READ MORE http://perclass.com/doc/guide/classifiers.html#sdtree
sdtree
is referenced in examples: