Table 2.

Model performance for categorical outcomes as indicated by area under the receiver operator characteristic curve values, and continuous outcomes as indicated by root mean square error and R² values

	AUC (95% CI)
	Logistic	Elastic net	Random forest
Binge-eating abstinence
10 repeated CV	0.50 (0.48–0.52)	0.51 (0.48–0.53)	0.49 (0.47–0.52)
Bootstrap	0.50 (0.49–0.52)	0.53 (0.52–0.54)	0.50 (0.49–0.51)
Optimism bootstrap	0.60 (0.59–0.61)	0.59 (0.58–0.60)	0.93 (0.92–0.94)
Weight loss ⩾5%
10 repeated CV	0.66 (0.64–0.69)	0.68 (0.66–0.70)	0.59 (0.56–0.61)
Bootstrap	0.61 (0.59–0.62)	0.63 (0.62–0.65)	0.56 (0.55–0.57)
Optimism bootstrap	0.71 (0.70–0.73)	0.73 (0.72–0.74)	0.94 (0.93–0.95)

AUC. area under the receiver operator characteristic curve; RMSE, root mean square error, 10 repeated CV, repeated 10-fold cross-validation.

Note: Higher AUC values indicate greater predictive accuracy; lower RMSE values and higher R² values indicate greater predictive accuracy.