MILK: 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.

Features

• Random forests
• 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.
• Affinity propagation

License: MIT Author: Luis Pedro Coelho (with code from LibSVM and scikits.learn) Website: http://luispedro.org/software/milk API Documentation: http://packages.python.org/milk/

Contents:

• Supervised Classification
  • Example
  • Learners
  • Composition and Defaults
  • supervised Submodules
• Cross-validation
  • API Documentation
• Feature Normalisation and Selection
  • Simple Normalisations
  • Whiten
  • Stepwise Discriminant Analysis
• AdaBoost
  • Adaboost
  • API Documentation
• Clustering
  • K-Means
• Random Numbers
  • How milk handles random numbers
  • Functions that use random numbers
  • random and numpy.random
• Examples
  • Using Random Forests
• Parallel Processing
  • Jug Integration
  • Multiprocessing
• Extensions
• Benchmarks
  • Scikits.learn benchmark
  • Limitations of This Benchmark
• Milksets
  • Links
• API Documentation
  • Toplevel functions
  • Modules
  • Example
  • Submodules
  • Classifiers
  • Example
  • Unsupervised Learning

Indices and tables

• Index
• Search Page