scikit-learn/doc/tutorial/statistical_inference/index.rst

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Scikit-learn tutorial: statistical-learning for sientific data processing
==========================================================================
**Online version:** http://gaelvaroquaux.github.com/scikit-learn-tutorial/
**Zip file for off-line browsing:** https://github.com/GaelVaroquaux/scikit-learn-tutorial/zipball/gh-pages
.. topic:: Statistical learning
`Machine learning <http://en.wikipedia.org/wiki/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
<http://en.wikipedia.org/wiki/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 <http://www.scipy.org>`_, `scipy
<http://www.scipy.org>`_, `matplotlib
<http://matplotlib.sourceforge.net/>`_).
.. include:: big_toc_css.rst
.. 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
.. toctree::
:numbered:
:maxdepth: 2
settings
supervised_learning
model_selection
unsupervised_learning
putting_together
finding_help