<commit_before>![LeVar logo](img/levar.png)

# LeVar

Machine learning training and evaluation database.

[![Build Status](https://travis-ci.org/levarml/LeVar.svg?branch=master)](https://travis-ci.org/levarml/LeVar)
[![Coverage Status](https://coveralls.io/repos/github/levarml/LeVar/badge.svg?branch=master)](https://coveralls.io/github/levarml/LeVar?branch=master)
[![Join the chat at https://gitter.im/peoplepattern/LeVar](https://badges.gitter.im/peoplepattern/LeVar.svg)](https://gitter.im/peoplepattern/LeVar?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

## About

This is a database for storing and organizing your machine learning evaluation data.
It currently supports basic classification and regression datasets. You can store
your evaluation datasets, upload experiments against those datasets and view results.

Why would you need such a thing? A lot of data scientists do this: for *every* machine
learning project, you rewrite the same script to calculate precision, recall,
root-mean-squared-error, etc. You gather lots of good evaluation data, but then keep
it all of the place in `tmp` directories, lose track of what's where, and ultimately
it's a challenge to remember how your latest result relates to a benchmark you haven't
looked at in months. Moreover, even *if* you've responsibly kept a good lab notebook,
and you can see how your overall numbers compare on the same dataset across months,
you may not have the actual item-for-item predictions around to facilitate
comparative error analysis.

This project aims to solve all of those problems.

* It provides a database for storing evaluation datasets and experiments run against
  them, so that over time you can keep track of how your methods are doing against
  static, known data inputs.<commit_msg>#49 Adjust logo size<commit_after># LeVar

<img alt="LeVar logo" width=300 src=img/levar.png>

Machine learning training and evaluation database.

[![Build Status](https://travis-ci.org/levarml/LeVar.svg?branch=master)](https://travis-ci.org/levarml/LeVar)
[![Coverage Status](https://coveralls.io/repos/github/levarml/LeVar/badge.svg?branch=master)](https://coveralls.io/github/levarml/LeVar?branch=master)
[![Join the chat at https://gitter.im/peoplepattern/LeVar](https://badges.gitter.im/peoplepattern/LeVar.svg)](https://gitter.im/peoplepattern/LeVar?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

## About

This is a database for storing and organizing your machine learning evaluation data.
It currently supports basic classification and regression datasets. You can store
your evaluation datasets, upload experiments against those datasets and view results.

Why would you need such a thing? A lot of data scientists do this: for *every* machine
learning project, you rewrite the same script to calculate precision, recall,
root-mean-squared-error, etc. You gather lots of good evaluation data, but then keep
it all of the place in `tmp` directories, lose track of what's where, and ultimately
it's a challenge to remember how your latest result relates to a benchmark you haven't
looked at in months. Moreover, even *if* you've responsibly kept a good lab notebook,
and you can see how your overall numbers compare on the same dataset across months,
you may not have the actual item-for-item predictions around to facilitate
comparative error analysis.

This project aims to solve all of those problems.

* It provides a database for storing evaluation datasets and experiments run against
  them, so that over time you can keep track of how your methods are doing against
  static, known data inputs.
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