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Class: RegressionTree

Compact regression tree


ctree = compact(tree)


ctree = compact(tree) creates a compact version of tree.

Input Arguments


A regression tree created using fitrtree.

Output Arguments


A compact regression tree. ctree has class CompactRegressionTree. You can predict regressions using ctree exactly as you can using tree. However, since ctree does not contain training data, you cannot perform some actions, such as cross validation.


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Compare the size of a full regression tree model to the compacted model.

Load the carsmall data set. Consider Acceleration, Displacement, Horsepower, and Weight as predictor variables.

load carsmall
X = [Acceleration Cylinders Displacement Horsepower Weight];

Grow a regression tree using the entire data set.

Mdl = fitrtree(X,MPG)
Mdl = 
             ResponseName: 'Y'
    CategoricalPredictors: []
        ResponseTransform: 'none'
          NumObservations: 94

  Properties, Methods

Mdl is a RegressionTree model. It is a full model, that is, it stores information such as the predictor and response data fitrtree used in training. For a properties list of full regression tree models, see RegressionTree.

Create a compact version of the full regression tree. That is, one that contains enough information to make predictions only.

CMdl = compact(Mdl)
CMdl = 
             ResponseName: 'Y'
    CategoricalPredictors: []
        ResponseTransform: 'none'

  Properties, Methods

CMdl is a CompactRegressionTree model. For a properties list of compact regression tree models, see CompactRegressionTree.

Inspect the amounts of memory that the full and compact regression trees consume.

mdlInfo = whos('Mdl');
cMdlInfo = whos('CMdl');
[mdlInfo.bytes cMdlInfo.bytes]
ans = 1×2

       12401        6898

ans = 0.5562

In this case, the compact regression tree model consumes about 25% less memory than the full model consumes.