lau_knn_crosserr

USE:

[missrate,gen,stdevGE] = lau_knn_crosserr(X,y,cvsets,k)

DESCRIPTION:

Computes the performance of a k-nearest neighbor classifier by n-fold crossvalidation. The global misclassification rate, the confusion matrix and its standard deviation are computed. At each fold, a disjoint fraction of data (1/cvsets) are predicted while the remaining fraction (1-1/cvsets) is used as prototypes. The confusion matrix is computed for each fold and summed up to form the global confusion matrix, gen.

ARGUMENTS:

X must be an mxp matrix (p>=1) and y an m rows vector. y corresponds to the class variable while X with the feature vector. Values of y must be integers from 1 to Nc (number of classes).

k is the number of neighbors on which decision is taken. 

cvsets is the number of folds in crossvalidation.

VALUES:

gen is the crossvalidated confusion matrix, missrate is the misclassification rate (1-sum of diagonal elements of gen/sum of all elements of gen)  while stdevGE is the standard deviation matrix associated with the gen matrix.

EXAMPLES:

//load the iris data set. There are 3 classes and 50 samples in each

> irisdata

//use the iris data set to evaluate the 5-fold crosvalidated performance of a 10-nearest neighbor classifier

>[missrate,gen,stdevGE] = lau_knn_crosserr(X,y,5,10)

COMMENTS:

Created by Laurentiu Adi Tarca. Revised 10.07.2003

This help was created on 12.03.2004