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

## Format

S4 object.

## Details

Loss Function: $$L(y, f(x)) = \frac{1}{2}( y - f(x))^2$$ Gradient: $$\frac{\delta}{\delta f(x)}\ L(y, f(x)) = f(x) - y$$ Initialization: $$\hat{f}^{[0]}(x) = \mathrm{arg~min}{c\in\mathrm{R}}{\mathrm{arg~min}}\ \frac{1}{n}\sum\limits_{i=1}^n L\left(y^{(i)}, c\right) = \bar{y}$$

## Usage

LossQuadratic$new() LossQuadratic$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: