Table 3 Performance of each method of OPLSR _a, OPLSR _b, FDR, and Lasso is shown with respect to each parameter (q-value for FDR, λ for Lasso, and α_f for the other two). The precision and the number of selected variables were denoted by P and N, respectively. The boldfaced numbers indicate the ones which outperformed the others

From: Feature selection using distributions of orthogonal PLS regression vectors in spectral data

Method		MSE	Q²	P	N
OPLSR_a	α_f=0.01	12,332	0.86	0.871	11.0
	α_f=0.05	8,989	0.89	0.701	16.2
	α_f=0.10	6,829	0.91	0.640	22.7
OPLSR_b	α_f=0.01	10,896	0.88	0.915	11.7
	α_f=0.05	8,704	0.91	0.860	28.3
	α_f=0.10	7,081	0.92	0.818	41.2
FDR	q=0.01	33,038	0.64	0.750	9.0
	q=0.05	12,123	0.86	0.714	36.6
	q=0.10	11,194	0.85	0.536	40.1
Lasso	λ=0.01	7,120	0.91	0.545	6.6
	λ=0.10	10,796	0.89	0.477	4.0
	λ=0.50	14,028	0.81	0.496	2.2

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