# LossBinomial: 0-1 Loss for binary classification derived of the binomial... In compboost: C++ Implementation of Component-Wise Boosting

## Description

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)))

\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\}}

## Usage

 1 2 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.

## Details

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.

## Examples

 1 2 3 # Create new loss object: bin.loss = LossBinomial\$new() bin.loss 

compboost documentation built on May 2, 2019, 6:40 a.m.