Table 5. Mean and SE of the prediction accuracy values for the parametric and the nonparametric methods for the F₂ population with heritability h² = 0.30.

F2, h2 = 0.30, Accuracy	Additive Mean	Epistatic Mean	Additive SE	Epistatic SE
Least squares regression	0.33	0.09	0.06	0.06
Ridge regression	0.50	−0.01	0.05	0.07
Bayesian ridge regression	0.50	−0.01	0.05	0.07
BLUP	0.50	−0.01	0.05	0.07
Lasso	0.50	−0.01	0.05	0.07
Bayes Lasso	0.50	0.00	0.05	0.07
Bayes A	0.50	0.00	0.05	0.07
Bayes B	0.50	0.00	0.05	0.07
Bayes C	0.50	0.00	0.05	0.07
Bayes Cπ	0.50	−0.01	0.05	0.07
Nadaraya-Watson estimator	0.40	0.16	0.05	0.07
RKHS	0.47	0.11	0.05	0.06
Support vector machine	0.47	0.14	0.05	0.07
Neural network	0.48	0.00	0.06	0.07

Mean and standard error of the prediction accuracy values for both the additive and the epistatic cases. The first 10 methods are parametric and the last four are nonparametric. The calculations for the epistatic mean and epistatic SE for the LASSO method are based on 184 replicates, for the epistatic mean and epistatic SE for the neural network method they are based on 498 replicates, and, for the rest, the calculations are based on 500 replicates.