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.
LeVar is sort of, but not really, an amalgam of “Model Eval”. Also, if you grew up in North America in the 1980s, or even if you didn’t, you should know that LeVar Burton is awesome for so many reasons.
LeVar is open source and licensed under the Apache License 2.0.
Developed with love at People Pattern Corporation