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# Table 1 Hyperparameter spaces used for tuning

From: Empowering individual trait prediction using interactions for precision medicine

Algorithm | Hyperparameter | Description | Values |
---|---|---|---|

glmnet | alpha | Elastic net mixing parameter. alpha = 1 is the LASSO, alpha = 0 is the ridge penalty | \(\left\{\mathrm{0,0.25,0.5,0.75,1}\right\}\) |

ranger | num.trees | Number of trees | 1000 |

mtry | Number of variables to possibly split at in each node | \(\left[\mathrm{1,100}\right]\subset {\mathbb{N}}\) | |

min.node.size | Minimal node size | \(\left[\mathrm{10,100}\right]\subset {\mathbb{N}}\) | |

MBMDRC | min.cell.size | Minimum number of samples with a specific genotype combination to be statistically relevant. If less, a cell is automatically labelled as \(O\) | \(\left[\mathrm{0,50}\right]\subset {\mathbb{N}}\) |

alpha | Significance level used to determine \(H\), \(L\) and \(O\) label of a cell | \(\left(\mathrm{0.01,1}\right)\subset {\mathbb{R}}\) | |

adjustment | Adjustment for lower order marginal effects | {NONE, CODOMINANT} | |

order | Number of SNPs to be considered in MDR models | \(\left\{\mathrm{1,2}\right\}\) | |

order.range | Use order as upper limit? | {TRUE, FALSE} | |

o.as.na | Use \(O\) labelled cells as NA or as the global probability/mean estimate | {TRUE, FALSE} |