With the rise of the data sciences, the scikit-learn, a BSD-licensed Python package for machine learning, is becoming an asset for more and more endeavors. Machine learning has traditionally been considered as very technical and inaccessible to the non mathematician. We are aiming to break this barrier.
The sprint will be focused on pragmatic down-to-earth improvements in the scikit. Our goal is to make it easy for people to contribute. A list of tasks and organization details can be found on the sprint planning wiki page. Amongst other things, we’ll be working on:
- integrating new learning algorithms, in particular merging in the many excellent pull requests that we have: hierarchical clustering, data transforming using linear discriminant analysis, multinomial naive bayes classifier …
- testing and logging framework,
- **better parallel computing support**,
- and many other itches to scratch, as it is a community-driven event.
Come and join us. It will be fun, and it’s an occasion to learn new tricks.