compact

Reduce size of classification tree model

Syntax

ctree = compact(tree)

Description

ctree = compact(tree) returns a CompactClassificationTree version of the trained classification tree model tree.

You can predict classifications using the CompactClassificationTree model object ctree in the same way as when you use tree. However, because ctree does not contain training data, you cannot perform certain tasks, such as cross-validation.

Examples

Create a Compact Classification Tree

Compare the size of the classification tree for Fisher's iris data to the compact version of the tree.

load fisheriris
fulltree = fitctree(meas,species);
ctree = compact(fulltree);
b = whos('fulltree'); % b.bytes = size of fulltree
c = whos('ctree'); % c.bytes = size of ctree
[b.bytes c.bytes] % shows ctree uses half the memory

ans = 1×2

       11714        5001

Input Arguments

tree — Full classification tree model

ClassificationTree model object

Full classification tree model, specified as a ClassificationTree model object trained with fitctree.

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