Description Format Details Usage Arguments Details Examples
This loss can be used for regression with y \in \mathrm{R}.
S4
object.
Loss Function:
L(y, f(x)) = | y - f(x)|
Gradient:
\frac{δ}{δ f(x)}\ L(y, f(x)) = \mathrm{sign}( f(x) - y)
Initialization:
\hat{f}^{[0]}(x) = \mathrm{arg~min}_{c\in R}\ \frac{1}{n}∑\limits_{i=1}^n L(y^{(i)}, c) = \mathrm{median}(y)
1 2 |
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.
This class is a wrapper around the pure C++
implementation. To see
the functionality of the C++
class visit
https://schalkdaniel.github.io/compboost/cpp_man/html/classloss_1_1_absolute_loss.html.
1 2 3 | # Create new loss object:
absolute.loss = LossAbsolute$new()
absolute.loss
|
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