compact

Compact regression tree

Syntax

ctree = compact(tree)

Description

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

Examples

Reduce Memory Consumption of Regression Tree Model

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 = 
  RegressionTree
             ResponseName: 'Y'
    CategoricalPredictors: []
        ResponseTransform: 'none'
          NumObservations: 94

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 = 
  CompactRegressionTree
             ResponseName: 'Y'
    CategoricalPredictors: []
        ResponseTransform: 'none'

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

       12570        7067

cMdlInfo.bytes/mdlInfo.bytes

ans = 0.5622

In this case, the compact regression tree model uses approximately half the memory that the full model uses.

Input Arguments

tree — Regression tree

RegressionTree object

Regression tree, specified as a RegressionTree object created by the fitrtree function.

Output Arguments

ctree — Compact regression tree

CompactRegressionTree object

Compact regression tree, returned as a CompactRegressionTree object. You can predict regressions using ctree exactly as you can using tree. However, because ctree does not contain training data, you cannot perform some actions, such as cross validation.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a