# [−][src]Module gbdt::fitness

This module implements some math functions used for gradient boosting process.

## Functions

AUC | AUC (Area Under the Curve) calculation for first n element in data vector. See wikipedia for detailed algorithm. |

MAE | MAE (Mean Absolute Error) calculation for first n element in data vector. See wikipedia for detail for detailed algorithm. |

RMSE | RMSE (Root-Mean-Square deviation) calculation for first n element in data vector. See wikipedia for detailed algorithm. |

almost_equal | Comparing two number with default floating error threshold. |

almost_equal_thrs | Comparing two number with a costomized floating error threshold. |

average | Return the weighted target average for first n data in data vector. |

label_average | Return the weighted label average for first n data in data vector. |

lad_loss | LAD loss function. |

lad_loss_gradient | LAD gradient calculation. |

logit | Logistic value function. |

logit_loss | Negative binomial log-likelyhood loss function. |

logit_loss_gradient | Log-likelyhood gradient calculation. |

same | Return whether the first n data in data vector have same target values. |

weighted_label_median | Return the weighted label median for first n data in data vector. |

weighted_residual_median | Return the weighted residual median for first n data in data vector. |