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After wonderful feedback on my
previous post on Scikit-learn
from the guys at
/r/MachineLearning,
I decided to collect the list of machine learning libraries into this
seperate note. Let me know if there’s a library that should be
included here.
Python
- Scikit-learn: comprehensive and easy
to use, I wrote a whole article
on why I like this library.
- PyBrain: Neural networks are one thing
that are missing from SciKit-learn, but this module makes up for
it.
- nltk: really useful if you’re doing
anything NLP or text mining related.
- Theano:
efficient computation of mathematical expressions using
GPU. Excellent for deep learning.
- Pylearn2: machine
learning toolbox built on top of Theano - in very early stages of
development.
- MDP (Modular toolkit for
Data Processing): a framework that is useful when setting up
workflows.
Java
- Spark: Apache’s new upstart,
supposedly up to a hundred times faster than Hadoop, now includes
MLLib, which contains a good selection of machine learning
algorithms, including classification, clustering and recommendation
generation. Currently undergoing rapid development. Development can
be in Python as well as JVM languages.
- Mahout: Apache’s machine learning
framework built on top of Hadoop, this looks promising, but comes
with all the baggage and overhead of Hadoop.
- Weka: this is a Java
based library with a graphical user interface that allows you to
run experiments on small datasets. This is great if you restrict
yourself to playing around to get a feel for what is possible with
machine learning. However, I would avoid using this in production
code at all costs: the API is very poorly designed, the algorithms
are not optimised for production use and the documentation is often
lacking.
- Mallet: another Java based library
with an emphasis on document classification. I’m not so familiar
with this one, but if you have to use Java this is bound to be
better than Weka.
- JSAT:
stands for “Java Statistical Analysis Tool” - created by Edward
Raff and was born out of his frustation with Weka (I know the
feeling). Looks pretty cool.
.NET
- Accord.NET: this
seems to be pretty comprehensive, and comes recommended by
primaryobjects on
Reddit. There is perhaps a slight slant towards image processing
and computer vision, as it builds on the popular library
AForge.NET for this purpose.
- Another option is to use one of the Java libraries compiled to .NET
using IKVM - I have used this approach
with success in production.
C++
- Vowpal Wabbit:
designed for very fast learning and released under a BSD license,
this comes recommended by
terath on Reddit.
- MultiBoost: a fast C++ framework
implementing some boosting algorithms as well as some cascades
(like the Viola-Jones cascades). It’s mainly focused on AdaBoost.MH
so it is multi-class/multi-label.
- Shogun: large machine
learning library with a focus on kernel methods and support vector
machines. Bindings to Matlab, R, Octave and Python.
General
- LibSVM and
LibLinear:
these are C libraries for support vector machines; there are also
bindings or implementations for many other languages. These are the
libraries used for support vector machine learning in Scikit-learn.
Conclusion
This article is a work in progress, so please send me your comments or
criticisms!
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