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Class: classregtree

Cut categories

classregtree will be removed in a future release. See fitctree, fitrtree, ClassificationTree, or RegressionTree instead.


C = cutcategories(t)
C = cutcategories(t,nodes)


C = cutcategories(t) returns an n-by-2 cell array C of the categories used at branches in the decision tree t, where n is the number of nodes. For each branch node i based on a categorical predictor variable x, the left child is chosen if x is among the categories listed in C{i,1}, and the right child is chosen if x is among those listed in C{i,2}. Both columns of C are empty for branch nodes based on continuous predictors and for leaf nodes.

C = cutcategories(t,nodes) takes a vector nodes of node numbers and returns the categories for the specified nodes.


Create a classification tree for car data:

load carsmall

t = classregtree([MPG Cylinders],Origin,...
                 'names',{'MPG' 'Cyl'},'cat',2)
t = 
Decision tree for classification
 1  if Cyl=4 then node 2 elseif Cyl in {6 8} then node 3 else USA
 2  if MPG<31.5 then node 4 elseif MPG>=31.5 then node 5 else USA
 3  if Cyl=6 then node 6 elseif Cyl=8 then node 7 else USA
 4  if MPG<21.5 then node 8 elseif MPG>=21.5 then node 9 else USA
 5  if MPG<41 then node 10 elseif MPG>=41 then node 11 else Japan
 6  if MPG<17 then node 12 elseif MPG>=17 then node 13 else USA
 7  class = USA
 8  class = France
 9  class = USA
10  class = Japan
11  class = Germany
12  class = Germany
13  class = USA

C = cutcategories(t)
C = 
    [4]    [1x2 double]
     []              []
    [6]    [         8]
     []              []
     []              []
     []              []
     []              []
     []              []
     []              []
     []              []
     []              []
     []              []
     []              []
ans =
     6     8


[1] Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984.

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