Machine Learning Toolkit
Machine Learning in Python
Milk is a machine learning toolkit in Python.
Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.
For unsupervised learning, milk supports k-means clustering and affinity propagation.
Milk is flexible about its inputs. It optimised for numpy arrays, but can often handle anything (for example, for SVMs, you can use any dataype and any kernel and it does the right thing).
There is a strong emphasis on speed and low memory usage. Therefore, most of the performance sensitive code is in C++. This is behind Python-based interfaces for convenience.
To learn more, check the docs at http://packages.python.org/milk/ or the code demos included with the source at milk/demos/.
New in 0.3.8
Fix compilation on Windows
New in 0.3.7
Source demos included (in source and documentation)
Add cluster agreement metrics
Fix nfoldcrossvalidation bug when using origins
New in 0.3.6
Unsupervised (1-class) kernel density modeling
Fix for when SDA returns empty
weights option to some learners
Adaboost (result of above changes)
New in 0.3.5
Fixes for 64 bit machines
Functions in measures.py all have same interface now.
New in 0.3.4
Random forest learners
Decision trees sped up 20x
Much faster gridsearch (finds optimum without computing all folds)
Self organising maps
SVMs. Using the libsvm solver with a pythonesque wrapper around it.
Stepwise Discriminant Analysis for feature selection.
Non-negative matrix factorisation
K-means using as little memory as possible.
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