Expand description
This module implements some math functions used for gradient boosting process.
Functions
AUC (Area Under the Curve) calculation for first n element in data vector.
See wikipedia for detailed algorithm.
MAE (Mean Absolute Error) calculation for first n element in data vector.
See wikipedia for detail for detailed algorithm.
RMSE (Root-Mean-Square deviation) calculation for first n element in data vector.
See wikipedia for detailed algorithm.
Comparing two number with default floating error threshold.
Comparing two number with a costomized floating error threshold.
Return the weighted target average for first n data in data vector.
Return the weighted label average for first n data in data vector.
LAD loss function.
LAD gradient calculation.
Logistic value function.
Negative binomial log-likelyhood loss function.
Log-likelyhood gradient calculation.
Return whether the first n data in data vector have same target values.
Return the weighted label median for first n data in data vector.
Return the weighted residual median for first n data in data vector.