crossval

Cross-validated decision tree

Syntax

cvmodel = crossval(model)

cvmodel = crossval(model,Name,Value)

Description

cvmodel = crossval(model) creates a partitioned model from model, a fitted classification tree. By default, crossval uses 10-fold cross validation on the training data to create cvmodel.

cvmodel = crossval(model,Name,Value) creates a partitioned model with additional options specified by one or more Name,Value pair arguments.

Examples

Create a Cross-Validation Model

Create a classification model for the ionosphere data, then create a cross-validation model. Evaluate the quality the model using kfoldLoss.

load ionosphere
tree = fitctree(X,Y);
cvmodel = crossval(tree);
L = kfoldLoss(cvmodel)

L = 0.1083

Input Arguments

model — Classification model

ClassificationTree object

Classification model, specified as a ClassificationTree object. Use the fitctree function to create a classification tree object.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: cvmodel = crossval(model,'Holdout',0.2)

CVPartition — Cross-validation partition

[] (default) | cvpartition object

Cross-validation partition, specified as the comma-separated pair consisting of 'CVPartition' and a cvpartition object created by the cvpartition function. crossval splits the data into subsets with cvpartition.

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Holdout — Fraction of data for holdout validation

scalar value in the range (0,1)

Fraction of the data used for holdout validation, specified as the comma-separated pair consisting of 'Holdout' and a scalar value in the range (0,1).

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Example: 'Holdout',0.3

Data Types: single | double

KFold — Number of folds

10 (default) | positive integer value greater than 1

Number of folds to use in a cross-validated model, specified as the comma-separated pair consisting of 'KFold' and a positive integer value greater than 1.

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Example: 'KFold',3

Data Types: single | double

Leaveout — Leave-one-out cross-validation flag

'off' (default) | 'on'

Leave-one-out cross-validation flag, specified as the comma-separated pair consisting of 'Leaveout' and 'on' or 'off'. Leave-one-out is a special case of 'KFold' in which the number of folds equals the number of observations.

Use only one of these four options at a time: 'CVPartition', 'Holdout', 'KFold', or 'Leaveout'.

Example: 'Leaveout','on'

Output Arguments

cvmodel — Partitioned model

ClassificationPartionedModel object

Partitioned model, returned as a ClassificationPartitionedModel object.

Tips

Assess the predictive performance of model on cross-validated data using the “kfold” methods and properties of cvmodel, such as kfoldLoss.

Alternatives

You can create a cross-validation tree directly from the data, instead of creating a decision tree followed by a cross-validation tree. To do so, include one of these five options in fitctree: 'CrossVal', 'KFold', 'Holdout', 'Leaveout', or 'CVPartition'.

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