cvpartition class -

Data partitions for cross-validation

Description

An object of the cvpartition class defines a random partition on a set of data of a specified size. You can be use this partition to define test and training sets for validating a statistical model using cross-validation.

Construction

cvpartition

Create cross-validation partition for data

Methods

disp	Display `cvpartition` object
display	Display `cvpartition` object
repartition	Repartition data for cross-validation
test	Test indices for cross-validation
training	Training indices for cross-validation

Properties

N	Number of observations (including observations with missing `group` values)
NumTestSets	Number of test sets
TestSize	Size of each test set
TrainSize	Size of each training set
Type	Type of partition

Copy Semantics

Value. To learn how this affects your use of the class, see Comparing Handle and Value Classes in the MATLAB Object-Oriented Programming documentation.

Examples

Use a 10-fold stratified cross-validation to compute the misclassification error for classify on iris data.

load('fisheriris');
CVO = cvpartition(species,'k',10);
err = zeros(CVO.NumTestSets,1);
for i = 1:CVO.NumTestSets
    trIdx = CVO.training(i);
    teIdx = CVO.test(i);
    ytest = classify(meas(teIdx,:),meas(trIdx,:),...
		 species(trIdx,:));
    err(i) = sum(~strcmp(ytest,species(teIdx)));
end
cvErr = sum(err)/sum(CVO.TestSize);