56 lines
1.9 KiB
ReStructuredText
56 lines
1.9 KiB
ReStructuredText
==========================================================================
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Scikit-learn tutorial: statistical-learning for sientific data processing
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==========================================================================
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**Online version:** http://gaelvaroquaux.github.com/scikit-learn-tutorial/
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**Zip file for off-line browsing:** https://github.com/GaelVaroquaux/scikit-learn-tutorial/zipball/gh-pages
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.. topic:: Statistical learning
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`Machine learning <http://en.wikipedia.org/wiki/Machine_learning>`_ is
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a technique with a growing importance, as the
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size of the datasets experimental sciences are facing is rapidly
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growing. Problems it tackles range from building a prediction function
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linking different observations, to classifying observations, or
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learning the structure in an unlabeled dataset.
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This tutorial will explore `statistical learning`, that is the use of
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machine learning techniques with the goal of `statistical inference
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<http://en.wikipedia.org/wiki/Statistical_inference>`_:
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drawing conclusions on the data at hand.
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``scikits.learn`` is a Python module integrating classic machine
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learning algorithms in the tightly-knit world of scientific Python
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packages (`numpy <http://www.scipy.org>`_, `scipy
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<http://www.scipy.org>`_, `matplotlib
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<http://matplotlib.sourceforge.net/>`_).
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.. include:: big_toc_css.rst
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.. note:: This document is meant to be used with **scikit-learn version 0.7+**.
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.. warning::
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In scikit-learn release 0.9, the import path has changed from
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`scikits.learn` to `sklearn`. To import with cross-version
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compatibility, use::
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try:
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from sklearn import something
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except ImportError:
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from scikits.learn import something
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.. toctree::
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:numbered:
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:maxdepth: 2
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settings
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supervised_learning
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model_selection
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unsupervised_learning
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putting_together
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finding_help
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