|
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
|
