Peekaboo

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

Recently some segmentation and superpixel algorithms I implemented were merged into scikits-image . You can see the example here . I reimplemented Felzenszwalb's fast graph based method , quickshift and SLIC . The goal was to have easy access to some successful methods to make comparison easier and encourage experimenting with the algorithms. Here is a a comparison of my implementations against the original implementations on Lena (downscaled by a factor of 2). The first row is my implementation, the second the original.