Description:

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. It works over many datatypes, with a preference for numpy arrays.

For unsupervised learning, milk supports k-means clustering and affinity propagation.

Changes to previous version:

• Added a new module: milk.ext.jugparallel to interface with jug (http://luispedro.org/software/jug). This makes it easy to parallelise things such as n-fold cross validation (each fold runs on its own processor) or multiple kmeans random starts.

• Add some new functions: measures.curves.precision_recall, milk.unsupervised.kmeans.select_best.kmeans.

• Fixed a tricky bug in SDA and a few minor issues elsewhere

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None, Agnostic

Tags: Python, Svm, Feature Selection, Kmeans, Decision Tree Learning, Random Forests, Supervised, Libsvm, Affinity Propagation, Nonnegative Matrix Factorization

Archive: download here

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