# Loss.jl

**Repository Path**: Julialang/Loss.jl

## Basic Information

- **Project Name**: Loss.jl
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-03-12
- **Last Updated**: 2022-03-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Loss.jl
=======

General functions for estimating loss functions inspired by Kaggle's release
of code for many common metrics. This package implements the full Cartesian
product of two ways of classifying loss functions:

* Single element loss function definitions:
	* Absolute deviation
	* Squared error
	* 0/1 loss
	* Hinge loss
	* Log loss
* Aggregation mechanism across elements:
	* Mean
	* Root Mean
	* Median
	* Minimum
	* Maximum

# To Do

* Add other metrics
	* AUC
* Add convenient abbreviations
	* rmse
	* ...