testc
TESTC
Test classifier, error / performance estimation
[E,C] = TESTC(A*W,TYPE)
[E,C] = TESTC(A,W,TYPE)
E = A*W*TESTC([],TYPE)
[E,F] = TESTC(A*W,TYPE,LABEL)
[E,F] = TESTC(A,W,TYPE,LABEL)
E = A*W*TESTC([],TYPE,LABEL)
Input  A  Dataset  W  Trained classifier mapping  TYPE  Type of performance estimate, default: probability of error  LABEL  Target class, default: none 
Output  E  Error / performance estimate  C  Number of erroneously classified objects per class. They are sorted according to A.LABLIST  F  Error / performance estimate of the nontarget classes 
Description This routine supplies several performance estimates for a trained classifier W based using a test dataset A. A should have objects for all classes assigned by W. Class prior probabilities given in A are taken into account. Use TESTD is just the number of incorrectly assigned objects have to be determied.
It is possible to supply a cell array of datasets {A*W}, or a cell array of datasets {A} or a cell array of classifiers {W}. In case A as well as W is a cell array, W might be 2dimensional with as many columns as A has datasets. See DISPERROR for an example.
Objects in A belonging to different classes than defined for W as well as unlabeled objects are neglected. Note that this implies that TESTC applied to a rejecting classifier (e.g. REJECTC) estimates the performance on the not rejected objects only. By [E,C] = TESTC(A,W); E = (C./CLASSSIZES(A))*GETPRIOR(A)';
the classification error with respect to all objects in A may be computed. Use CONFMAT for an overview of the total class assignment including the unlabeled (rejected) objects.
In case of missing classes in A, [E,C] = TESTC(A*W) returns in E a NaN but in C still the number of erroneously classified objects per class.
If LABEL is given, the performance estimate relates just to that class as target class. If LABEL is not given a class average is returned weighted by the class priors.
The following performance measures are supported for TYPE
'crisp'  Expected classification error based on error counting, weighted by the class priors (default).  'FN'  E False negative F False positive  'TP'  E True positive F True negative  'soft'  Expected classification error based on soft error summation, i.e. a sum of the absolute difference between classifier output and target, weighted by class priors.  'F'  Lissack and Fu error estimate  'mse'  Expected mean square difference between classifier output and target (based on soft labels), weighted by class priors.  'auc'  Area under the ROC curve (this is an error and not a performance!). For multi class problems this is the weigthed average (by class priors) of the oneagainstrest contributions of the classes.  'precision'  E Fraction of true target objects among the objects classified as target. The target class is defined by LABEL. Priors are not used. F Recall, fraction of correctly classified objects in the target class. Priors are not used.  'sensitivity'  E Fraction of correctly classified objects in the target class (defined by LABEL). Priors are not used. Sensitivity as used her is identical to recall. F Specificity, fraction non target objects that are not classified into the target class (defined by LABEL). Priors are not used. 
Example(s)
prex_plotc, See also
mappings, datasets, confmat, rejectc, This file has been automatically generated. If badly readable, use the helpcommand in Matlab. 
