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Machine Learning Cheat Sheet (for scikit-learn)
- aidos 14y agoI know nothing much about machine learning (though I was digging around scikit learn earlier this evening) but this is a diagram I can appreciate :)
- gms 14y agoFor predicting structure (which takes you to 'tough luck' in the chart as it's not implemented in scikit-learn), you can use structured SVM's through either SVM-Struct (http://www.cs.cornell.edu/people/tj/svm_light/svm_struct.html http://www.cs.cornell.edu/people/tj/svm_light/svm_struct.htm...) or JLIS (http://flake.cs.uiuc.edu/~mchang21/softwares/JLIS/indirect.html http://flake.cs.uiuc.edu/~mchang21/softwares/JLIS/indirect.h...). Though note that both libraries are free for non-commercial use only.
- t3kcit 14y agoOr you could use pystruct: https://github.com/amueller/pystruct https://github.com/amueller/pystruct But I saved that for another blog post ;) That is BSD licensed and pure Python, has some more feature than SVMstruct but is not as optimized (yet).
- gms 14y agoI had no idea your project existed; thanks for sharing.
- t3kcit 14y agoI did a another post on that one. http://peekaboo-vision.blogspot.de/2013/01/pystruct-more-structured-prediction.html http://peekaboo-vision.blogspot.de/2013/01/pystruct-more-str... The post still needs some more detail, though.
- StavrosK 14y agoI had a short look at scikit-learn for a hobby classifier I'm writing (it aims to email me interesting HN articles so I don't waste much time reading things I don't like or refreshing HN all the time), and it looks fantastic. I haven't looked at the code or used it very much, but the documentation is very thorough and informative, and the library itself is very extensive. There are some things missing, (e.g. The naive Bayes implementation is offline, which is not very useful to me), but the library is fantastic for prototyping, at least. Congratulations to the people working on it!