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Figure 4. Graph illustrates examples of binary classification. One sample is treated as an unknown subject for training with the remaining samples. On the left, in the binary differentiation between the active NPSLE group ( ) and the control group ( ), the unknown subject ( ), which in fact represents a patient with active NPSLE, has an MDA score that is far from zero (score, -7.54). Thus, we can be sure of the classification, and, indeed, the subject was correctly assigned to the active NPSLE group. In the middle, in the binary differentiation between the active NPSLE group and the past NPSLE group ( ), the unknown subject, which in fact also represents a patient with active NPSLE (score, -1.41), has an MDA score that is nearer to the mean score of the active NPSLE group. This low score and the position in the region of overlap tell us that confidence in the classification was low. Nonetheless, the subject was correctly assigned. On the right, in the binary differentiation between the active NPSLE group and the past NPSLE group, the unknown subject, which in fact represents a patient with past NPSLE (score, -0.42), has an MDA score that is nearer to the mean score of the active NPSLE group. Confidence in the classification was low, however, and the subject was incorrectly assigned.
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) groups, have considerable overlap and classification errors. 
