Decision Tree in Matlab. The documentation page of the function classregtree is self explanatory. Lets go over some of the most common parameters of the classification tree model x data matrix, rows are instances, cols are predicting attributesy column vector, class label for each instancecategorical specify which attributes are discrete type as opposed to continuousmethod whether to produce classification or regression tree depend on the class typenames gives names to the attributesprune enabledisable reduced error pruningminparentminleaf allows to specify min number of instances in a node if it is to be further splitnvartosample used in random trees consider K randomly chosen attributes at each nodeweights specify weighted instancescost specify cost matrix penalty of the various errorssplitcriterion criterion used to select the best attribute at each split. Free Download Game Blacklight Tango Down more. Im only familiar with the Gini index which is a variation of the Information Gain criterion. A complete example to illustrate the process load data. MPG Cylinders Horsepower ModelYear. MPG Cylinders Horsepower ModelYear mixed continousdiscrete data. Origin class labels. Predicted evalt, x. Predicted confusion matrix. N sumcm. err N sumdiagcm N testing error. Na. N. prediction evaltt, inst pred Japan. Iruvar 1997 Movie more. Update The above classregtree class was made obsolete, and is superseded by Classification. Tree and Regression. Tree classes in R2. R2. 01. 4a. Here is the updated example, using the new functionsclasses t fitctreex, y, Predictor. Names,vars,. Categorical. Predictors,Cylinders, ModelYear, Prune,off. Level,3. predicttt, 3. How To Use Patch In Matlab' title='How To Use Patch In Matlab' />Legend. Orientation,orientation creates a legend with the legend items arranged in the specified orientation. Resumen. El presente trabajo tiene por objetivo brindar un enfoque teorico sobre la resolucion del modelo presadepredador y su implementacion en MatLab.