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

Format

S4 object.

Details

Loss Function: $$ L(y, f(x)) = | y - f(x)| $$ Gradient: $$ \frac{\delta}{\delta f(x)}\ L(y, f(x)) = -\mathrm{sign}(y - f(x)) $$ Initialization: $$ \hat{f}^{[0]}(x) = \mathrm{arg~min}_{c\in R}\ \frac{1}{n}\sum\limits_{i=1}^n L(y^{(i)}, c) = \mathrm{median}(y) $$

Usage

LossAbsolute$new()
LossAbsolute$new(offset)

Arguments

offset [numeric(1)]

Numerical value which can be used to set a custom offset. If so, this value is returned instead of the loss optimal initialization.

Examples

# Create new loss object: absolute_loss = LossAbsolute$new() absolute_loss
#> #> LossAbsolute Loss: #> #> Loss function: L(y,x) = |y - f(x)| #> #> #>