perfCv

Cross-validation accuracy of given dataset and classifier

Contents

Syntax

Description

vRr=perfCv(DS, classifier, classifierOpt) returns the cross-validation recognition rate of the given dataset and classifier.

• vRr: validating recognition rate
• DS: Dataset
  • DS.input: Input data (each column is a feature vector)
  • DS.output: Output class (ranging from 1 to N)
• classifierOpt: Training parameters for the classifier

vRr=perfCv(DS, classifier, classifierOpt, 1) also plots the dataset and misclasified instances (if the dimension is 2).

[vRrOverall, tRrOverall, vRr, tRr, computedClass]=perfCv(...) returns more info about cross-validation:

• vRrOverall: Overall validating RR
• tRrOverall: Overall training RR
• vRr: Validating RR for all folds
• tRr: Training RR for all folds
• computedClass: The computed class of each data instance in DS

Example

DS=prData('iris');
showPlot=1;
foldNum=10;
classifier='qc';
vRr=perfCv(DS, classifier, [], foldNum, showPlot);

See Also

perfCv4classifier, perfLoo.

Published with MATLAB® R2018a