Scikit-Learn 0.13 released! We want your feedback.

After a little delay, the team finished work on the 0.13 release of scikit-learn.
There is also a user survey that we launched in parallel with the release, to get some feedback from our users.

There is a list of changes and new features on the website.
You can upgrade using easy-install or pip using:

pip install -U scikit-learn
or
easy_install -u scikit-learn


There were more than 60 people contributing to this release, with 24 people having 10 commits or more.

Again many improvements are behind the scenes or only slightly notable. We improved test coverage a lot and we have much more consistent parameter names now. There is now also a user guide entry for the classification metrics, and their naming was improved.

This was one of the many improvements Arnaud Joly, who joined the project very recently but nevertheless wound up being the one with the second most commits in this release!

Now let me get to some of the more visible highlights of this release from my perspective:

- Thanks to Lars and Olivier, the Hashing Trick finally made it into scikit-learn.
  This allows for very fast vectorization of large text corpora and stateless transformers for the same.

- Sample weights were added to the tree module thanks to Noel and Gilles. This enabled the implementation of a smarter resampling for random forests, which leads to a speed-up of random forests of up to a factor of two! Also, this is the basis of including AdaBoost with Trees in the next release.

- I added a method to use totally randomized trees for hashing / embedding features to a high-dimensional, sparse binary representation. It goes along the lines of my last blog post on using non-linear embeddings followed by simple linear classifiers.

- I also added Nystroem kernel approximations, which are really easy to do but should come in quite handy. They still need some more work, though. For details, see my post on kernel approximations.


Thanks to the team for working on this together. I am really happy with the way everybody joins forces, this is an amazing project!

Comments

Post a Comment

Popular posts from this blog

Machine Learning Cheat Sheet (for scikit-learn)

Image
As you hopefully have heard, we at scikit-learn are doing a user survey (which is still open by the way). One of the requests there was to provide some sort of flow chart on how to do machine learning. As this is clearly impossible, I went to work straight away. This is the result: [edit2] clarification: With ensemble classifiers and ensemble regressors I mean random forests , extremely randomized trees, gradient boosted trees , and the soon-to-be-come weight boosted trees (adaboost). [/edit2] Needless to say, this sheet is completely authoritative.
Read more

A Wordcloud in Python

Image
Last week I was at Pycon DE , the German Python conference. After hacking on scikit-learn a lot last week, I decided to to something different on my way back, that I had planned for quite a while: doing a wordl -like word cloud . I know, word clouds are a bit out of style but I kind of like them any way. My motivation to think about word clouds was that I thought these could be combined with topic-models to give somewhat more interesting visualizations. So I looked around to find a nice open-source implementation of word-clouds ... only to find none. (This has been a while, maybe it has changed since). While I was bored in the train last week, I came up with this code . A little today-themed taste:
Read more

MNIST for ever....

Image
[update] This post is a bit old, but many people still seem interested. So just a short update: Nowadays I would use Python and scikit-learn to do this. Here is an example of how to do cross-validation for SVMs in scikit-learn. Scikit-learn even downloads MNIST for you. [/update] MNIST is, for better or worse, one of the standard benchmarks for machine learning and is also widely used in then neural networks community as a toy vision problem. Just for the unlikely case that anyone is not familiar with it: It is a dataset of handwritten digits, 0-9, in black on white background. It looks something like this: There are 60000 training and 10000 test images, each 28x28 gray scale. There are roughly the same number of examples of each category in the test and training datasets. I used it in some papers myself even though there are some reasons why it is a little weird. Some not-so-obvious (or maybe they are) facts are: - The images actually contain a 20x20 patch of digi
Read more

Python things you never need: Empty lambda functions

Ever wanted to create an empty Python function in-line? No? Me neither. But my coworker does... We found the answer: <pre> x = lambda : None </pre> If you ever needed this, please tell me ;) [Edit] Apparently some people do need it :) Btw, most people seem to try Most people try lambda: pass first. But you have to remember, the thing at the right side of the colon is the return value of the function, not the body! So an expression is needed, not a statement. [\Edit]
Read more