F_{2}, h^{2} = 0.70, Accuracy | Additive Mean | Epistatic Mean | Additive SE | Epistatic SE |
---|---|---|---|---|

Least squares regression | 0.56 | 0.09 | 0.05 | 0.06 |

Ridge regression | 0.80 | 0.02 | 0.02 | 0.07 |

Bayesian ridge regression | 0.80 | 0.01 | 0.02 | 0.07 |

BLUP | 0.80 | 0.01 | 0.02 | 0.08 |

LASSO | 0.82 | −0.01 | 0.02 | 0.05 |

Bayes LASSO | 0.81 | 0.01 | 0.02 | 0.07 |

Bayes A | 0.81 | 0.00 | 0.02 | 0.07 |

Bayes B | 0.81 | 0.01 | 0.02 | 0.07 |

Bayes C | 0.81 | 0.01 | 0.02 | 0.07 |

Bayes Cπ | 0.83 | 0.01 | 0.02 | 0.07 |

Nadaraya-Watson estimator | 0.67 | 0.35 | 0.04 | 0.06 |

RKHS | 0.76 | 0.29 | 0.03 | 0.05 |

Support vector machine | 0.78 | 0.33 | 0.03 | 0.07 |

Neural network | 0.77 | 0.05 | 0.03 | 0.09 |

Mean and SE 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 213 replicates, for the epistatic mean and epistatic SE for the neural network method they are based on 493 replicates, and, for the rest, the calculations are based on 500 replicates.