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.