This loss can be used for binary classification. The coding we have chosen here acts on $$y \in \{-1, 1\}$$.

## Format

S4 object.

## Details

Loss Function: $$L(y, f(x)) = \log(1 + \mathrm{exp}(-2yf(x)))$$ Gradient: $$\frac{\delta}{\delta f(x)}\ L(y, f(x)) = - \frac{y}{1 + \mathrm{exp}(2yf)}$$ Initialization: $$\hat{f}^{[0]}(x) = \frac{1}{2}\mathrm{log}(p / (1 - p))$$ with $$p = \frac{1}{n}\sum\limits_{i=1}^n\mathrm{1}_{\{y^{(i)} = 1\}}$$

## Usage

LossBinomial$new() LossBinomial$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:
bin_loss = LossBinomial\$new()
bin_loss#>
#> LossBinomial Loss:
#>
#>   Loss function: L(y,x) = log(1 + exp(-2yf(x))
#>
#>
#>