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Scikit-learn Tutorial: Statistical-Learning for Scientific Data Processing

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Zip file for off-line browsing: https://github.com/GaelVaroquaux/scikit-learn-tutorial/zipball/gh-pages

Statistical learning

Machine learning is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset.

This tutorial will explore statistical learning, that is the use of machine learning techniques with the goal of statistical inference: drawing conclusions on the data at hand.

scikits.learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib).

Note

This document is meant to be used with scikit-learn version 0.7+.

Warning

In scikit-learn release 0.9, the import path has changed from scikits.learn to sklearn. To import with cross-version compatibility, use:

try:
    from sklearn import something
except ImportError:
    from scikits.learn import something

 


Originally posted at gael-varoquaux.info/

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