Estimating misclassification error: a closer look at cross-validation based methods

BMC Research Notes

Table 4 Results for the microarray example

Method	n	ē ^a	ē _N	$\overset{―}{M S E}$	SD(MSE)	$\overset{―}{V A R}$	SD(VAR)	MSB	$\overset{―}{B I A S}$	SD(BIAS)
LOOCV	50	0.2363	0.2404	0.0056	0.0077	0	0	0.0056	0.0042	0.0746
BCVn	50	0.2363	0.1726	0.0104	0.0070	0.0037	0.0013	0.0067	−0.0636	0.0517
k CVn/2	50	0.2363	0.2418	0.0056	0.0074	0.0003	0.0001	0.0053	0.0055	0.0729
BCVn/2	50	0.2363	0.1736	0.0102	0.0069	0.0037	0.0011	0.0065	−0.0627	0.0511
k CV10	50	0.2363	0.2454	0.0057	0.0070	0.0007	0.0003	0.0050	0.0091	0.0704
BCV10	50	0.2363	0.1772	0.0098	0.0066	0.0037	0.0010	0.0061	−0.0591	0.0508

$\overset{―}{e} a = (1 / N) \sum_{i = 1}^{N} e_{i}, {\overset{―}{e}}_{N} = (1 / N) \sum_{i = 1}^{N} {\overset{―}{e}}_{Ri}, M S B = (1 / N) \sum_{i = 1}^{N} {({\overset{―}{e}}_{Ri} - e_{i})}^{2} .$

ISSN: 1756-0500