Description Format Details Usage Arguments Details Examples
This loss can be used for binary classification. The coding we have chosen here acts on y \in \{-1, 1\}.
S4
object.
Loss Function:
L(y, f(x)) = \log(1 + \mathrm{exp}(-2yf(x)))
Gradient:
\frac{δ}{δ f(x)}\ L(y, f(x)) = - \frac{y}{1 + \mathrm{exp}(2yf)}
Initialization:
\hat{f}^{[0]}(x) = \frac{1}{2}\mathrm{log}(p / (1 - p))
with
p = \frac{1}{n}∑\limits_{i=1}^n\mathrm{1}_{\{y^{(i)} = 1\}}
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_binomial_loss.html.
1 2 3 | # Create new loss object:
bin.loss = LossBinomial$new()
bin.loss
|
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