This loss can be used for regression with \(y \in \mathrm{R}\).
Format
S4 object.
Arguments
- offset
(
numeric(1)|matrix())
Numerical value or matrix to set a custom offset. If used, this value is returned instead of the loss optimal initialization.- quantile
(
numeric(1))
Numerical value between 0 and 1 that defines the quantile that is modeled.
Details
Loss Function: $$ L(y, f(x)) = h| y - f(x)| $$ Gradient: $$ \frac{\delta}{\delta f(x)}\ L(y, f(x)) = -h\mathrm{sign}( y - f(x)) $$ Initialization: $$ \hat{f}^{[0]}(x) = \mathrm{arg~min}_{c\in R}\ \frac{1}{n}\sum\limits_{i=1}^n L(y^{(i)}, c) = \mathrm{quantile}(y, q) $$
Usage
LossAbsolute$new()
LossAbsolute$new(quantile)
LossAbsolute$new(offset, quantile)Inherited methods from Loss
$loss():matrix(), matrix() -> matrix()$gradient():matrix(), matrix() -> matrix()$constInit():matrix() -> matrix()$calculatePseudoResiduals():matrix(), matrix() -> matrix()$getLossType():() -> character(1)
Examples
# Create new loss object:
quadratic_loss = LossQuadratic$new()
quadratic_loss
#> LossQuadratic: L(y,x) = 0.5 * (y - f(x))^2
#>