# cutcategories

Class: classregtree

Cut categories

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

## Syntax

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

## Description

`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.

## Examples

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 view(t)```

```C = cutcategories(t) C = [4] [1x2 double] [] [] [6] [ 8] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] C{1,2} ans = 6 8```

## References

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

## See Also

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