Table 1.

Model performance on “Sick” task

Model	Training Regime	Precision	Recall	F1-Score	AUPR
				(95% CI)	(95% CI)
J4.8	Prototype	0.48	0.99	0.65	0.83
				(0.63-0.67)	(0.81-0.85)
Logistic regression	Biased	0.05	0.94	0.10	0.63
				(0.09-0.11)	(0.55-0.76)
Logistic regression	Gold	0.83	0.88	0.85	0.90
				(0.83-0.87)	(0.88-0.92)
Logistic regression	Silver	0.85	0.88	0.87	0.91
				(0.85-0.88)	(0.90-0.93)
Random forest	Biased	0.04	0.91	0.07	0.59
				(0.06-0.09)	0.54-0.70
Random forest	Gold	0.36	0.89	0.51	0.81
				(0.38-0.68)	(0.78-0.84)
Random forest	Silver	0.70	0.88	0.78	0.87
				(0.66-0.85)	(0.85-0.89)
SVM	Biased	0.09	0.95	0.16	0.82
				(0.13-0.20)	(0.79-0.87)
SVM	Gold	0.33	0.93	0.49	0.88
				(0.37-0.67)	(0.85-0.91)
SVM	Silver	0.96	0.74	0.83	0.93
				(0.81-0.85)	(0.92-0.95)

The underlined value represents the final selected model from among the variants. This is the model we further analyze in the error analysis. Because the bootstrap distribution of some test statistics exhibited non-normal behavior, their corresponding confidence intervals are wider.