Table 2.

Classification results for the generative embedding procedure. Shown are key performance measures of the classification algorithm, including: balanced accuracy, area under the curve, sensitivity (recall), specificity, positive predictive value (precision), and negative predictive value. Performance measures are shown for the three different binary classifications (i.e., CHR vs. REM, CHR vs. IMP, and IMP vs. REM).

Classification	CHR (n = 15) vs. REM (n = 39)	CHR (n = 15) vs. IMP (n = 31)	IMP (n = 31) vs. REM (n = 39)
Accuracy	0.87	0.63	0.63
Balanced accuracy	0.79	0.47	0.61
Area under the curve (AUC)	0.87	0.35	0.63
Sensitivity (recall)	0.97	0.94	0.77
Specificity	0.60	0	0.45
Positive predictive value (Precision)	0.86	0.66	0.64
Negative predictive value	0.90	0	0.61