

Update on scikit-learn: recent developments for machine learning in Python - ohe
http://gael-varoquaux.info/blog/?p=165

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cf
Since it isn't likely going to be said otherwise, I really think this is the
best library for doing Machine Learning out there right now.

This assessment isn't based on breadth of algorithms supported, since R beats
it here. It has nothing to do with documentation, even though it has the best.
Scikits.learn is fantastic because it has consistent interfaces. The creators
of this library have thought very hard about what interfaces classifiers
should have. This greatly reduces the learning curve and makes it cake to
compare classifiers.

The clean interfaces make it easier to perform cross-validation and leads to
less surprises. The largest problem with most machine learning code out there
is while it works, it never gets this kind of software engineering attention.

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amueller
Thanks for this great feedback :)

What algorithms do you think scikit-learn is still missing compared to R?

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cf
Largely, what I need is integration with JAGS <http://mcmc-
jags.sourceforge.net/> which is a niche that pymc fills. I am working on a PR
to get in the other stuff I need.

I bring up R more as a point that there is more to a library than supporting
lots of algorithms. R wins by that metric
[http://www.cran.r-project.org/web/packages/available_package...](http://www.cran.r-project.org/web/packages/available_packages_by_date.html)

~~~
mblondel_ml
The comparison doesn't really make sense: R is a language (and environment),
scikit-learn is a library.

