Table 2. Mean accuracy rate and area under the receiver operating curve (AUC) for the eight classifiers on VBM grey matter.

	Algorithm	Mean accuracy	5%limit	95%limit	AUC	5%limit	95%limit
Healthy vs. Schizophrenia	aridge	0.756	0.701	0.806	0.836	0.788	0.885
	blasso	0.741	0.683	0.792	0.82	0.769	0.872
	celastic	0.76	0.703	0.812	0.815	0.762	0.869
	dL0 norm	0.752	0.712	0.795	0.835	0.785	0.884
	eSVC	0.772	0.731	0.812	0.85	0.804	0.896
	fRDA	0.745	0.683	0.804	---	---	---
	gGPC	0.756	0.699	0.805	0.828	0.778	0.878
	hRF	0.752	0.69	0.805	0.837	0.788	0.885
Healthy vs. Bipolar dis.	ridge	0.623	0.586	0.664	0.686	0.621	0.75
	lasso	0.655	0.616	0.7	0.702	0.639	0.766
	elastic	0.635	0.592	0.681	0.691	0.627	0.756
	L0 norm	0.651	0.613	0.69	0.706	0.643	0.769
	SVC	0.647	0.599	0.694	0.698	0.634	0.762
	RDA	0.616	0.557	0.668	---	---	---
	GPC	0.608	0.565	0.658	0.671	0.605	0.737
	RF	0.62	0.571	0.67	0.688	0.624	0.753
Bipolar dis. vs. Schizophrenia	ridge	0.66	0.605	0.716	0.692	0.627	0.756
	lasso	0.609	0.555	0.659	0.646	0.579	0.713
	elastic	0.616	0.562	0.676	0.689	0.624	0.753
	L0 norm	0.581	0.507	0.643	0.659	0.593	0.726
	SVC	0.652	0.593	0.712	0.696	0.632	0.761
	RDA	0.605	0.545	0.657	---	---	---
	GPC	0.621	0.583	0.661	0.696	0.632	0.76
	RF	0.613	0.581	0.646	0.685	0.621	0.75

Lower and upper limits for the 95% confidence intervals generated by bootstrap are also reported for these two quantities.

^aridge: Ridge regression,

^blasso: Lasso regression,

^celastic: Elastic net regularization,

^dL0-norm: L0-norm regularization,

^eSVC: Support vector classifier,

^fRDA: Regularized discriminant analysis,

^gGPC: Gaussian process classifier,

^hRF: Random forest.