This loss can be used for regression with $$y \in \mathrm{R}$$.

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: $$\hat{f}^{}(x) = \mathrm{arg~min}_{c\in R}\ \frac{1}{n}\sum\limits_{i=1}^n L(y^{(i)}, c) = \mathrm{quantile}(y, q)$$

## 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: