Update on scikit-learn: recent developments for machine learning in Python

by ohe • 14 years ago

9 8 14 years ago

cf2• 14 years ago

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.

amueller2• 14 years ago

Thanks for this great feedback :)

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

natekupp• 14 years ago

It'd be really great if you guys had an implementation of Friedman's Multivariate Adaptive Regression Splines[1]. For me, this is the only R package left for which I don't have a Python alternative.

[1] http://www.salford-systems.com/doc/MARS.pdf

cf2• 14 years ago

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...

Update on scikit-learn: recent developments for machine learning in Python

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