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<h3>Contents</h3>
<ul>
<li><a class="reference internal" href="#">0.6</a><ul>
<li><a class="reference internal" href="#changelog">Changelog</a></li>
<li><a class="reference internal" href="#people">People</a></li>
</ul>
</li>
<li><a class="reference internal" href="#changes-0-5">0.5</a><ul>
<li><a class="reference internal" href="#id3">Changelog</a><ul>
<li><a class="reference internal" href="#new-classes">New classes</a></li>
<li><a class="reference internal" href="#documentation">Documentation</a></li>
<li><a class="reference internal" href="#fixes">Fixes</a></li>
<li><a class="reference internal" href="#examples">Examples</a></li>
<li><a class="reference internal" href="#external-dependencies">External dependencies</a></li>
<li><a class="reference internal" href="#removed-modules">Removed modules</a></li>
<li><a class="reference internal" href="#misc">Misc</a></li>
</ul>
</li>
<li><a class="reference internal" href="#authors">Authors</a></li>
</ul>
</li>
<li><a class="reference internal" href="#id4">0.4</a><ul>
<li><a class="reference internal" href="#id5">Changelog</a></li>
<li><a class="reference internal" href="#id6">Authors</a></li>
</ul>
</li>
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<div class="section" id="changes-0-6">
<span id="id1"></span><h1>0.6<a class="headerlink" href="#changes-0-6" title="Permalink to this headline">¶</a></h1>
<p>scikits.learn 0.6 was released on december 2010. It is marked by the
inclusion of several new modules and a general renaming of old
ones. It is also marked by the inclusion of new example, including
applications to real-world datasets.</p>
<p><div style="text-align: center; margin: 0px 0 -5px 0;"> <a class="reference external" href="auto_examples/applications/plot_face_recognition.html"><img alt="banner1" src="auto_examples/applications/images/plot_face_recognition.png" style="height: 150px;" /></a> <a class="reference external" href="auto_examples/linear_model/plot_species_distribution.html"><img alt="banner2" src="auto_examples/applications/images/plot_species_distribution_modeling.png" style="height: 150px;" /></a> <a class="reference external" href="auto_examples/gaussian_process/plot_gp_regression.html"><img alt="banner3" src="auto_examples/gaussian_process/images/plot_gp_regression.png" style="height: 150px;" /></a> <a class="reference external" href="auto_examples/linear_model/plot_lasso_lars.html"><img alt="banner4" src="auto_examples/linear_model/images/plot_sgd_iris.png" style="height: 150px;" /></a> </div></p>
<div class="section" id="changelog">
<h2>Changelog<a class="headerlink" href="#changelog" title="Permalink to this headline">¶</a></h2>
<blockquote>
<ul class="simple">
<li>New <a class="reference external" href="http://scikit-learn.sourceforge.net/modules/sgd.html">stochastic gradient</a> descent
module by Peter Prettenhofer. The module comes with complete
documentation and examples.</li>
<li>Improved svm module: memory consumption has been reduced by 50%,
heuristic to automatically set class weights, possibility to
assign weights to samples (see
<a class="reference internal" href="auto_examples/svm/plot_weighted_samples.html#example-svm-plot-weighted-samples-py"><em>SVM: Weighted samples</em></a> for an example).</li>
<li>New <a class="reference internal" href="modules/gaussian_process.html#gaussian-process"><em>Gaussian Processes</em></a> module by Vincent Dubourg. This module
also has great documentation and some very neat examples. See
<a class="reference internal" href="auto_examples/gaussian_process/plot_gp_regression.html#example-gaussian-process-plot-gp-regression-py"><em>Gaussian Processes regression: basic introductory example</em></a> or
<a class="reference internal" href="auto_examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.html#example-gaussian-process-plot-gp-probabilistic-classification-after-regression-py"><em>Gaussian Processes classification example: exploiting the probabilistic output</em></a>
for a taste of what can be done.</li>
<li>It is now possible to use liblinear’s Multi-class SVC (option
multi_class in <tt class="xref py py-class docutils literal"><span class="pre">svm.LinearSVC</span></tt>)</li>
<li>New features and performance improvements of text feature
extraction.</li>
<li>Improved sparse matrix support, both in main classes
(<tt class="xref py py-class docutils literal"><span class="pre">grid_search.GridSearchCV</span></tt>) as in modules
scikits.learn.svm.sparse and scikits.learn.linear_model.sparse.</li>
<li>Lots of cool new examples and a new section that uses real-world
datasets was created. These include:
<a class="reference internal" href="auto_examples/applications/plot_face_recognition.html#example-applications-plot-face-recognition-py"><em>Faces recognition example using eigenfaces and SVMs</em></a>,
<a class="reference internal" href="auto_examples/applications/plot_species_distribution_modeling.html#example-applications-plot-species-distribution-modeling-py"><em>Species distribution modeling</em></a>,
<a class="reference internal" href="auto_examples/applications/svm_gui.html#example-applications-svm-gui-py"><em>Libsvm GUI</em></a>,
<a class="reference internal" href="auto_examples/applications/wikipedia_principal_eigenvector.html#example-applications-wikipedia-principal-eigenvector-py"><em>Wikipedia princial eigenvector</em></a> and
others.</li>
<li>Faster <a class="reference internal" href="modules/linear_model.html#least-angle-regression"><em>Least Angle Regression</em></a> algorithm. It is now 2x
faster than the R version on worst case and up to 10x times faster
on some cases.</li>
<li>Faster coordinate descent algorithm. In particular, the full path
version of lasso (<tt class="xref py py-func docutils literal"><span class="pre">linear_model.lasso_path()</span></tt>) is more than
200x times faster than before.</li>
<li>It is now possible to get probability estimates from a
<tt class="xref py py-class docutils literal"><span class="pre">linear_model.LogisticRegression</span></tt> model.</li>
<li>module renaming: the glm module has been renamed to linear_model,
the gmm module has been included into the more general mixture
model and the sgd module has been included in linear_model.</li>
<li>Lots of bug fixes and documentation improvements.</li>
</ul>
</blockquote>
</div>
<div class="section" id="people">
<h2>People<a class="headerlink" href="#people" title="Permalink to this headline">¶</a></h2>
<p>People that made this release possible preceeded by number of commits:</p>
<blockquote>
<ul class="simple">
<li>207 <a class="reference external" href="http://twitter.com/ogrisel">Olivier Grisel</a></li>
<li>167 <a class="reference external" href="http://fseoane.net/blog/">Fabian Pedregosa</a></li>
<li>97 <a class="reference external" href="http://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a></li>
<li>68 <a class="reference external" href="http://www-sop.inria.fr/members/Alexandre.Gramfort/index.fr.html">Alexandre Gramfort</a></li>
<li>59 <a class="reference external" href="http://www.mblondel.org/journal/">Mathieu Blondel</a></li>
<li>55 <a class="reference external" href="http://gael-varoquaux.info/blog/">Gael Varoquaux</a></li>
<li>33 Vincent Dubourg</li>
<li>21 <a class="reference external" href="http://www.ee.columbia.edu/~ronw/">Ron Weiss</a></li>
<li>9 Bertrand Thirion</li>
<li>3 <a class="reference external" href="http://atpassos.posterous.com">Alexandre Passos</a></li>
<li>3 Anne-Laure Fouque</li>
<li>2 Ronan Amicel</li>
<li>1 <a class="reference external" href="http://osdf.github.com/">Christian Osendorfer</a></li>
</ul>
</blockquote>
</div>
</div>
<div class="section" id="changes-0-5">
<span id="id2"></span><h1>0.5<a class="headerlink" href="#changes-0-5" title="Permalink to this headline">¶</a></h1>
<div class="section" id="id3">
<h2>Changelog<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
<div class="section" id="new-classes">
<h3>New classes<a class="headerlink" href="#new-classes" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>Support for sparse matrices in some classifiers of modules
<tt class="docutils literal"><span class="pre">svm</span></tt> and <tt class="docutils literal"><span class="pre">linear_model</span></tt> (see <tt class="xref py py-class docutils literal"><span class="pre">svm.sparse.SVC</span></tt>,
<tt class="xref py py-class docutils literal"><span class="pre">svm.sparse.SVR</span></tt>, <tt class="xref py py-class docutils literal"><span class="pre">svm.sparse.LinearSVC</span></tt>,
<tt class="xref py py-class docutils literal"><span class="pre">linear_model.sparse.Lasso</span></tt>, <tt class="xref py py-class docutils literal"><span class="pre">linear_model.sparse.ElasticNet</span></tt>)</li>
<li>New <tt class="xref py py-class docutils literal"><span class="pre">pipeline.Pipeline</span></tt> object to compose different estimators.</li>
<li>Recursive Feature Elimination routines in module
<a class="reference internal" href="modules/feature_selection.html#feature-selection-doc"><em>Feature selection</em></a>.</li>
<li>Addition of various classes capable of cross validation in the
linear_model module (<tt class="xref py py-class docutils literal"><span class="pre">linear_model.LassoCV</span></tt>, <tt class="xref py py-class docutils literal"><span class="pre">linear_model.ElasticNetCV</span></tt>,
etc.).</li>
<li>New, more efficient LARS algorithm implementation. The Lasso
variant of the algorithm is also implemented. See
<tt class="xref py py-class docutils literal"><span class="pre">linear_model.lars_path</span></tt>, <tt class="xref py py-class docutils literal"><span class="pre">linear_model.LARS</span></tt> and
<tt class="xref py py-class docutils literal"><span class="pre">linear_model.LassoLARS</span></tt>.</li>
<li>New Hidden Markov Models module (see classes
<tt class="xref py py-class docutils literal"><span class="pre">hmm.GaussianHMM</span></tt>, <tt class="xref py py-class docutils literal"><span class="pre">hmm.MultinomialHMM</span></tt>,
<tt class="xref py py-class docutils literal"><span class="pre">hmm.GMMHMM</span></tt>)</li>
<li>New module feature_extraction (see <a class="reference internal" href="modules/classes.html#feature-extraction-ref"><em>class reference</em></a>)</li>
<li>New FastICA algorithm in module scikits.learn.fastica</li>
</ul>
</blockquote>
</div>
<div class="section" id="documentation">
<h3>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>Improved documentation for many modules, now separating
narrative documentation from the class reference. As an example,
see <a class="reference external" href="http://scikit-learn.sourceforge.net/modules/svm.html">documentation for the SVM module</a> and the
complete <a class="reference external" href="http://scikit-learn.sourceforge.net/modules/classes.html">class reference</a>.</li>
</ul>
</blockquote>
</div>
<div class="section" id="fixes">
<h3>Fixes<a class="headerlink" href="#fixes" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>API changes: adhere variable names to PEP-8, give more
meaningful names.</li>
<li>Fixes for svm module to run on a shared memory context
(multiprocessing).</li>
<li>It is again possible to generate latex (and thus PDF) from the
sphinx docs.</li>
</ul>
</blockquote>
</div>
<div class="section" id="examples">
<h3>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>new examples using some of the mlcomp datasets:
<a class="reference internal" href="auto_examples/mlcomp_sparse_document_classification.html#example-mlcomp-sparse-document-classification-py"><em>Classification of text documents: using a MLComp dataset</em></a>,
<em class="xref std std-ref">example_mlcomp_document_classification.py</em></li>
<li>Many more examaples. <a class="reference external" href="http://scikit-learn.sourceforge.net/auto_examples/index.html">See here</a>
the full list of examples.</li>
</ul>
</blockquote>
</div>
<div class="section" id="external-dependencies">
<h3>External dependencies<a class="headerlink" href="#external-dependencies" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>Joblib is now a dependencie of this package, although it is
shipped with (scikits.learn.externals.joblib).</li>
</ul>
</blockquote>
</div>
<div class="section" id="removed-modules">
<h3>Removed modules<a class="headerlink" href="#removed-modules" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>Module ann (Artificial Neural Networks) has been removed from
the distribution. Users wanting this sort of algorithms should
take a look into pybrain.</li>
</ul>
</blockquote>
</div>
<div class="section" id="misc">
<h3>Misc<a class="headerlink" href="#misc" title="Permalink to this headline">¶</a></h3>
<blockquote>
<ul class="simple">
<li>New sphinx theme for the web page.</li>
</ul>
</blockquote>
</div>
</div>
<div class="section" id="authors">
<h2>Authors<a class="headerlink" href="#authors" title="Permalink to this headline">¶</a></h2>
<p>The following is a list of authors for this release, preceeded by
number of commits:</p>
<blockquote>
<ul class="simple">
<li>262 Fabian Pedregosa</li>
<li>240 Gael Varoquaux</li>
<li>149 Alexandre Gramfort</li>
<li>116 Olivier Grisel</li>
<li>40 Vincent Michel</li>
<li>38 Ron Weiss</li>
<li>23 Matthieu Perrot</li>
<li>10 Bertrand Thirion</li>
<li>7 Yaroslav Halchenko</li>
<li>9 VirgileFritsch</li>
<li>6 Edouard Duchesnay</li>
<li>4 Mathieu Blondel</li>
<li>1 Ariel Rokem</li>
<li>1 Matthieu Brucher</li>
</ul>
</blockquote>
</div>
</div>
<div class="section" id="id4">
<h1>0.4<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h1>
<div class="section" id="id5">
<h2>Changelog<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
<p>Major changes in this release include:</p>
<blockquote>
<blockquote>
<ul class="simple">
<li>Coordinate Descent algorithm (Lasso, ElasticNet) refactoring &
speed improvements (roughly 100x times faster).</li>
<li>Coordinate Descent Refactoring (and bug fixing) for consistency
with R’s package GLMNET.</li>
<li>New metrics module.</li>
<li>New GMM module contributed by Ron Weiss.</li>
<li>Implementation of the LARS algorithm (without Lasso variant for now).</li>
<li>feature_selection module redesign.</li>
<li>Migration to GIT as content management system.</li>
<li>Removal of obsolete attrselect module.</li>
<li>Rename of private compiled extensions (aded underscore).</li>
<li>Removal of legacy unmaintained code.</li>
<li>Documentation improvements (both docstring and rst).</li>
<li>Improvement of the build system to (optionally) link with MKL.</li>
</ul>
</blockquote>
<p>Also, provide a lite BLAS implementation in case no system-wide BLAS is
found.</p>
<blockquote>
<ul class="simple">
<li>Lots of new examples.</li>
<li>Many, many bug fixes ...</li>
</ul>
</blockquote>
</blockquote>
</div>
<div class="section" id="id6">
<h2>Authors<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
<p>The committer list for this release is the following (preceded by number
of commits):</p>
<blockquote>
<ul class="simple">
<li>143 Fabian Pedregosa</li>
<li>35 Alexandre Gramfort</li>
<li>34 Olivier Grisel</li>
<li>11 Gael Varoquaux</li>
<li>5 Yaroslav Halchenko</li>
<li>2 Vincent Michel</li>
<li>1 Chris Filo Gorgolewski</li>
</ul>
</blockquote>
</div>
</div>
</div>
</div>
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