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<h1>scikits.learn: machine learning in Python</h1><p><div style="text-align: center; margin: 0px 0 -5px 0;"> <a class="reference external" href="auto_examples/cluster/plot_affinity_propagation.html"><img alt="banner1" src="auto_examples/cluster/images/plot_affinity_propagation.png" style="height: 150px;" /></a> <a class="reference external" href="auto_examples/gaussian_process/plot_gp_regression.html"><img alt="banner2" src="auto_examples/gaussian_process/images/plot_gp_regression.png" style="height: 150px;" /></a> <a class="reference external" href="auto_examples/svm/plot_oneclass.html"><img alt="banner3" src="auto_examples/svm/images/plot_oneclass.png" style="height: 150px;" /></a> <a class="reference external" href="auto_examples/cluster/plot_lena_segmentation.html"><img alt="banner4" src="auto_examples/cluster/images/plot_lena_segmentation.png" style="height: 150px;" /></a> </div></p>
<div class="topic">
<p class="topic-title first">Easy-to-use and general-purpose machine learning in Python</p>
<p><tt class="docutils literal"><span class="pre">scikits.learn</span></tt> is a Python module integrating classic machine
learning algorithms in the tightly-knit world of scientific Python
packages (<a class="reference external" href="http://www.scipy.org">numpy</a>, <a class="reference external" href="http://www.scipy.org">scipy</a>, <a class="reference external" href="http://matplotlib.sourceforge.net/">matplotlib</a>).</p>
<p>It aims to provide simple and efficient solutions to learning
problems that are accessible to everybody and reusable in various
contexts: <strong>machine-learning as a versatile tool for science and
engineering</strong>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field"><th class="field-name">Features:</th><td class="field-body"><ul class="first simple">
<li><strong>Solid</strong>: <a class="reference internal" href="supervised_learning.html#supervised-learning"><em>Supervised learning</em></a>: <a class="reference internal" href="modules/svm.html#svm"><em>Support Vector Machines</em></a>, <a class="reference internal" href="modules/linear_model.html#linear-model"><em>Generalized Linear Models</em></a>.</li>
<li><strong>Work in progress</strong>: <a class="reference internal" href="unsupervised_learning.html#unsupervised-learning"><em>Unsupervised learning</em></a>:
<a class="reference internal" href="modules/clustering.html#clustering"><em>Clustering</em></a>, <a class="reference internal" href="modules/mixture.html#mixture"><em>Gaussian mixture models</em></a>, manifold learning, <a class="reference internal" href="modules/decompositions.html#ica"><em>ICA</em></a>, <a class="reference internal" href="modules/gaussian_process.html#gaussian-process"><em>Gaussian Processes</em></a></li>
<li><strong>Planed</strong>: Gaussian graphical models, matrix factorization</li>
</ul>
</td>
</tr>
<tr class="field"><th class="field-name">License:</th><td class="field-body"><p class="first last">Open source, commercially usable: <strong>BSD license</strong> (3 clause)</p>
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<h1>User Guide<a class="headerlink" href="#user-guide" title="Permalink to this headline">¶</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="install.html">1. Installing <cite>scikits.learn</cite></a><ul>
<li class="toctree-l2"><a class="reference internal" href="install.html#installing-an-official-release">1.1. Installing an official release</a></li>
<li class="toctree-l2"><a class="reference internal" href="install.html#third-party-distributions-of-scikits-learn">1.2. Third party distributions of scikits.learn</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">3.2. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">3.3. Stochastic Gradient Descent</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">3.5. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">3.6. Gaussian Processes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="unsupervised_learning.html">4. Unsupervised learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">4.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">4.2. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decompositions.html">4.3. Decomposing signals in components (matrix factorization problems)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="model_selection.html">5. Model Selection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">5.1. Cross-Validation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">5.2. Grid Search</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/classes.html">6. Class Reference</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#support-vector-machines">6.1. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#generalized-linear-models">6.2. Generalized Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#bayesian-regression">6.3. Bayesian Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#naive-bayes">6.4. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#nearest-neighbors">6.5. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#gaussian-mixture-models">6.6. Gaussian Mixture Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#hidden-markov-models">6.7. Hidden Markov Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#clustering">6.8. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#covariance-estimators">6.9. Covariance Estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#signal-decomposition">6.10. Signal Decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#cross-validation">6.11. Cross Validation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#grid-search">6.12. Grid Search</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#feature-selection">6.13. Feature Selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#feature-extraction">6.14. Feature Extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classes.html#pipeline">6.15. Pipeline</a></li>
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<h1>Example Gallery<a class="headerlink" href="#example-gallery" title="Permalink to this headline">¶</a></h1>
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<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#examples-based-on-real-world-datasets">Examples based on real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#clustering">Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#gaussian-process-for-machine-learning">Gaussian Process for Machine Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#generalized-linear-models">Generalized Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="auto_examples/index.html#gaussian-mixture-models">Gaussian Mixture Models</a></li>
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<h1>Development<a class="headerlink" href="#development" title="Permalink to this headline">¶</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="developers/index.html">Contributing</a><ul>
<li class="toctree-l2"><a class="reference internal" href="developers/index.html#submitting-a-bug-report">Submitting a bug report</a></li>
<li class="toctree-l2"><a class="reference internal" href="developers/index.html#retrieving-the-latest-code">Retrieving the latest code</a></li>
<li class="toctree-l2"><a class="reference internal" href="developers/index.html#contributing-code">Contributing code</a></li>
<li class="toctree-l2"><a class="reference internal" href="developers/index.html#coding-guidelines">Coding guidelines</a></li>
<li class="toctree-l2"><a class="reference internal" href="developers/index.html#apis-of-scikit-learn-objects">APIs of scikit learn objects</a></li>
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<li class="toctree-l1"><a class="reference internal" href="about.html">About us</a><ul>
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