Loss Description Arguments
LossQuadratic Quadratic differences between prediction and response. This loss corresponds to the Gaussian distribution.
offset
Custom offset for initializing the model
LossAbsolute Absolute differences between prediction and response. This loss corresponds to the Laplace distribution.
offset
Custom offset for initializing the model
LossQuantile Use this loss to boost arbitrary quantiles (robust regression).
offset
Custom offset for initializing the model
quantile
The quantile you like to boost
LossHuber Huber loss with quadratic loss in $$[-d,d]$$ and linear extrapolation outside.
offset
Custom offset for initializing the model
delta
Defining the interval in which the error is measured quadratically
LossBinomial Binary classification loss that corresponds to the binomial distribution with logit link. Labels are coded as -1 and 1.
offset
Custom offset for initializing the model
LossCustom Custom loss by using custom C++ functions.
loss
R function to calculates the loss (vectorized)
gradient
R function to calculate the gradient
constant_initializer
R function to compute the optimal constant initialization
LossCustomCpp Custom loss by using custom C++ functions.
loss_ptr
C++ pointer to a function that defines the loss
gradient_ptr
C++ pointer to a function to calculate the gradient
constant_initializer_ptr
C++ pointer to a function to compute the optimal constant initialization