Table 3. Mean classification accuracies obtained by combining all data features together, after dimensionality reduction based on Principal Component Analysis (PCA Based combination), and after selecting only the 1% of variables with largest t values (t-thresholded combination).

	Algorithm	PCA based combination			t-thresholded combination
		Mean accuracy	5%limit	95%limit	Mean accuracy	5%limit	95%limit
Healthy vs. schizophrenia	aridge	0.6	0.516	0.673	0.733	0.686	0.776
	blasso	0.638	0.584	0.698	0.781	0.747	0.816
	celastic	0.662	0.598	0.728	0.752	0.711	0.791
	dL0 norm	0.713	0.655	0.771	0.761	0.717	0.798
	eSVC	0.582	0.495	0.675	0.737	0.693	0.781
	fRDA	0.658	0.593	0.725	0.792	0.758	0.823
	gGPC	0.619	0.561	0.688	0.717	0.657	0.776
	hRF	0.674	0.624	0.725	0.729	0.657	0.792
Healthy vs. bipolar dis.	ridge	0.526	0.455	0.598	0.619	0.562	0.662
	lasso	0.553	0.5	0.612	0.596	0.561	0.639
	elastic	0.549	0.484	0.626	0.619	0.586	0.646
	L0 norm	0.557	0.495	0.62	0.564	0.518	0.618
	SVC	0.518	0.461	0.574	0.608	0.583	0.632
	RDA	0.529	0.464	0.603	0.608	0.563	0.663
	GPC	0.522	0.459	0.585	0.623	0.561	0.677
	RF	0.491	0.405	0.576	0.627	0.575	0.67
Bipolar dis. vs. schizophrenia	ridge	0.492	0.446	0.537	0.641	0.601	0.678
	lasso	0.56	0.506	0.614	0.594	0.557	0.637
	elastic	0.556	0.493	0.614	0.613	0.572	0.651
	L0 norm	0.579	0.507	0.65	0.609	0.564	0.657
	SVC	0.516	0.456	0.573	0.563	0.518	0.607
	RDA	0.571	0.513	0.628	0.613	0.564	0.661
	GPC	0.521	0.457	0.586	0.676	0.627	0.718
	RF	0.529	0.453	0.599	0.637	0.583	0.686

Limits for 95% confidence intervals are based on bootstrap.

^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.