Peekaboo

Recently we added another method for kernel approximation, the Nyström method, to scikit-learn , which will be featured in the upcoming 0.13 release. Kernel-approximations were my first somewhat bigger contribution to scikit-learn and I have been thinking about them for a while. To dive into kernel approximations, first recall the kernel-trick .

I'm a bit obsessed with MNIST. Mainly because I think it should not be used in any papers any more - it is weird for a lot of reasons. When preparing the workshop we held yesterday I noticed one that I wasn't aware of yet: most of the 1-vs-1 subproblems, are really easy! Basically all pairs of numbers can be separated perfectly using a linear classifier! And even you you just do a PCA to two dimensions, they can pretty much still be linearly separated! It doesn't get much easier than that. This makes me even more sceptical about "feature learning" results on this dataset. To illustrate my point, here are all pairwise PCA projections. The image is pretty huge. Otherwise you wouldn't be able to make out individual data points. You can generate it using this very simple gist . There are some classes that are not obviously separated: 3 vs 5, 4 vs 9, 5 vs 8 and 7 vs 9. But keep in mind, this is just a PCA to two dimensions. It doesn't mean that ...

Today there was a workshop at my uni, organized by my Professor Sven Behnke, together with my colleagues Hannes Schulz, Nenard Birešev and me. The target group was a local graduate school with a general scientific background, but not much CS or machine learning. The workshop consisted of us explaining the methods and the students then playing around with them and answering some questions using IPython notebooks that we provided (if you still don't know about IPython Notebooks, watch this talk now ). Using the notebooks worked out great! There is only so much you can teach in a 5 hour workshop but I think we got across some basic concepts of machine learning and working with data in Python. We got some positive feedback and the students really went exploring. We covered PCA, k-means, linear regression, logistic regression and nearest neighbors, including some real-world examples. You can find all resources, including tex and notebooks for generating figures etc. on gith...

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:

Recently Jake showed some pretty cool videos in his blog . This inspired me to go back to an idea I had some time ago, about visualizing high-dimensional data via random projections. I love to do exploratory data analysis with scikit-learn , using the manifold , decomposition and clustering module. But in the end, I can only look at two (or three) dimensions. And I really like to see what I am doing.

Recently I entered my first kaggle competition - for those who don't know it, it is a site running machine learning competitions. A data set and time frame is provided and the best submission gets a money prize, often something between 5000$ and 50000$. I found the approach quite interesting and could definitely use a new laptop, so I entered Detecting Insults in Social Commentary. My weapon of choice was Python with scikit-learn - for those who haven't read my blog before: I am one of the core devs of the project and never shut up about it.

Last night I uploaded the new version 0.12 of scikit-learn to pypi . Also the updated website is up and running and development now starts towards 0.13 . The new release has some nifty new features ( see whatsnew ): * Multidimensional scaling * Multi-Output random forests ( like these ) * Multi-task Lasso * More loss functions for ensemble methods and SGD * Better text feature extraction