SDCONFMAT Estimating confusion matrix from true labels and decision

    SDCONFMAT(LAB,DEC)
    CM=SDCONFMAT(LAB,DEC)
    CM=SDCONFMAT(LAB,DEC,options)

 Add confusion matrix entries as new labels (sample property 'confmat')
    DATA=SDCONFMAT(DATA,DEC)

 Confusion matrices at operating points OPS from soft output data OUT
    [CM,LL]=SDCONFMAT(OPS,OUT)

 INPUT
    LAB      SDLAB object with true labels
    DEC      SDLAB object with decisions
    OPS      SDOPS set of operating points
    OUT      SDDATA with soft classifier outputs

 OUTPUT
    CM       Confusion matrix (double)

 OPTIONS
  'norm'   - normalize the confusion matrix
  'full'   - create a square confusion matrix using all possible classifier decisions
             (performances on diagonal).
  'classes',CLASSLL - use only classes in CLASSLL (SDLIST,string array or cellstr)
  'decisions',DECLL - use only decisions in DECLL (SDLIST,string array or cellstr)
  'string' - return string with confusion matrix (for report generation)
  'no header' - return string without header lines

 READ MORE
 http://perclass.com/doc/guide/decisions.html#confmat

sdconfmat is referenced in examples:

• kb18: How to protect a trained discriminant against outliers?
• kb17: How to optimize three-class classifier in imbalanced problems
• kb13: How to find samples with a specific type of error in a confusion matrix?
• kb12: Detector classifier cascade with ROC analysis
• kb8: How to build a detector in a single line of code?