Error/performance estimation by cross validation (rotation)
[ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,NFOLDS,1,TESTFUN)
Cross validation estimation of the error or performance (defined by TESTFUN) of the untrained classifier CLASSF using the dataset A. The set is randomly permutated and divided in NFOLDS (almost) equally sized parts, using a stratified procedure. The classifier is trained on NFOLDS-1 parts and the remaining part is used for testing. This is rotated over all parts. ERR is their weighted avarage over the class priors. CERR are the class error frequencies. The inputs A and/or CLASSF may be cell arrays of datasets and classifiers. In that case ERR is an array with on position ERR(i,j) the error or performance of classifier j for dataset i. In this mode CERR and NLAB_OUT are returned in cell arrays.
For NREP > 1 the mean error(s) over the repetitions is returned in ERR and the standard deviations in the observed errors in STDS.
If NREP == 'DPS', crossvalidation is done by density preserving data splitting (DPS). In this case NFOLD should be a power of 2.
In case NREP == 0 an index array is returned pointing to a fold for every object. No training or testing is done. This is useful for handling training and testing outside CROSSVAL.
1. R. Kohavi: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI 1995: 1137-1145.