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<section id="older-versions">
<h1>Older Versions<a class="headerlink" href="#older-versions" title="Link to this heading">#</a></h1>
<section id="version-0-12-1">
<span id="changes-0-12-1"></span><h2>Version 0.12.1<a class="headerlink" href="#version-0-12-1" title="Link to this heading">#</a></h2>
<p><strong>October 8, 2012</strong></p>
<p>The 0.12.1 release is a bug-fix release with no additional features, but is
instead a set of bug fixes</p>
<section id="changelog">
<h3>Changelog<a class="headerlink" href="#changelog" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>Improved numerical stability in spectral embedding by <a class="reference external" href="http://gael-varoquaux.info">Gael
Varoquaux</a></p></li>
<li><p>Doctest under windows 64bit by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p>Documentation fixes for elastic net by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a> and
<a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a></p></li>
<li><p>Proper behavior with fortran-ordered NumPy arrays by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p>Make GridSearchCV work with non-CSR sparse matrix by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a></p></li>
<li><p>Fix parallel computing in MDS by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p>Fix Unicode support in count vectorizer by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a></p></li>
<li><p>Fix MinCovDet breaking with X.shape = (3, 1) by <a class="reference external" href="https://github.com/VirgileFritsch">Virgile Fritsch</a></p></li>
<li><p>Fix clone of SGD objects by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a></p></li>
<li><p>Stabilize GMM by <a class="reference external" href="https://github.com/VirgileFritsch">Virgile Fritsch</a></p></li>
</ul>
</section>
<section id="people">
<h3>People<a class="headerlink" href="#people" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>14 <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a></p></li>
<li><p>12 <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p>10 <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a></p></li>
<li><p>5 <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a></p></li>
<li><p>3 <a class="reference external" href="https://github.com/VirgileFritsch">Virgile Fritsch</a></p></li>
<li><p>1 <a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a></p></li>
<li><p>1 <a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a></p></li>
<li><p>1 <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a></p></li>
</ul>
</section>
</section>
<section id="version-0-12">
<span id="changes-0-12"></span><h2>Version 0.12<a class="headerlink" href="#version-0-12" title="Link to this heading">#</a></h2>
<p><strong>September 4, 2012</strong></p>
<section id="id1">
<h3>Changelog<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>Various speed improvements of the <a class="reference internal" href="../modules/tree.html#tree"><span class="std std-ref">decision trees</span></a> module, by
<a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingClassifier</span></code></a> now support feature subsampling
via the <code class="docutils literal notranslate"><span class="pre">max_features</span></code> argument, by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a>.</p></li>
<li><p>Added Huber and Quantile loss functions to
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingRegressor</span></code></a>, by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a>.</p></li>
<li><p><a class="reference internal" href="../modules/tree.html#tree"><span class="std std-ref">Decision trees</span></a> and <a class="reference internal" href="../modules/ensemble.html#forest"><span class="std std-ref">forests of randomized trees</span></a>
now support multi-output classification and regression problems, by
<a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a>.</p></li>
<li><p>Added <a class="reference internal" href="../modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelEncoder</span></code></a>, a simple utility class to
normalize labels or transform non-numerical labels, by <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>.</p></li>
<li><p>Added the epsilon-insensitive loss and the ability to make probabilistic
predictions with the modified huber loss in <a class="reference internal" href="../modules/sgd.html#sgd"><span class="std std-ref">Stochastic Gradient Descent</span></a>, by
<a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>.</p></li>
<li><p>Added <a class="reference internal" href="../modules/manifold.html#multidimensional-scaling"><span class="std std-ref">Multi-dimensional Scaling (MDS)</span></a>, by Nelle Varoquaux.</p></li>
<li><p>SVMlight file format loader now detects compressed (gzip/bzip2) files and
decompresses them on the fly, by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a>.</p></li>
<li><p>SVMlight file format serializer now preserves double precision floating
point values, by <a class="reference external" href="https://bsky.app/profile/ogrisel.bsky.social">Olivier Grisel</a>.</p></li>
<li><p>A common testing framework for all estimators was added, by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
<li><p>Understandable error messages for estimators that do not accept
sparse input by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p>Speedups in hierarchical clustering by <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a>. In
particular building the tree now supports early stopping. This is
useful when the number of clusters is not small compared to the
number of samples.</p></li>
<li><p>Add MultiTaskLasso and MultiTaskElasticNet for joint feature selection,
by <a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a>.</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">metrics.auc_score</span></code> and
<a class="reference internal" href="../modules/generated/sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score" title="sklearn.metrics.average_precision_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.average_precision_score</span></code></a> convenience functions by <a class="reference external" href="https://amueller.github.io/">Andreas
Müller</a>.</p></li>
<li><p>Improved sparse matrix support in the <a class="reference internal" href="../modules/feature_selection.html#feature-selection"><span class="std std-ref">Feature selection</span></a>
module by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
<li><p>New word boundaries-aware character n-gram analyzer for the
<a class="reference internal" href="../modules/feature_extraction.html#text-feature-extraction"><span class="std std-ref">Text feature extraction</span></a> module by <a class="reference external" href="https://github.com/kernc">@kernc</a>.</p></li>
<li><p>Fixed bug in spectral clustering that led to single point clusters
by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
<li><p>In <a class="reference internal" href="../modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>, added an option to
ignore infrequent words, <code class="docutils literal notranslate"><span class="pre">min_df</span></code> by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
<li><p>Add support for multiple targets in some linear models (ElasticNet, Lasso
and OrthogonalMatchingPursuit) by <a class="reference external" href="https://vene.ro/">Vlad Niculae</a> and
<a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a>.</p></li>
<li><p>Fixes in <code class="docutils literal notranslate"><span class="pre">decomposition.ProbabilisticPCA</span></code> score function by Wei Li.</p></li>
<li><p>Fixed feature importance computation in
<a class="reference internal" href="../modules/ensemble.html#gradient-boosting"><span class="std std-ref">Gradient-boosted trees</span></a>.</p></li>
</ul>
</section>
<section id="api-changes-summary">
<h3>API changes summary<a class="headerlink" href="#api-changes-summary" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>The old <code class="docutils literal notranslate"><span class="pre">scikits.learn</span></code> package has disappeared; all code should import
from <code class="docutils literal notranslate"><span class="pre">sklearn</span></code> instead, which was introduced in 0.9.</p></li>
<li><p>In <a class="reference internal" href="../modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve" title="sklearn.metrics.roc_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.roc_curve</span></code></a>, the <code class="docutils literal notranslate"><span class="pre">thresholds</span></code> array is now returned
with it’s order reversed, in order to keep it consistent with the order
of the returned <code class="docutils literal notranslate"><span class="pre">fpr</span></code> and <code class="docutils literal notranslate"><span class="pre">tpr</span></code>.</p></li>
<li><p>In <code class="docutils literal notranslate"><span class="pre">hmm</span></code> objects, like <code class="docutils literal notranslate"><span class="pre">hmm.GaussianHMM</span></code>,
<code class="docutils literal notranslate"><span class="pre">hmm.MultinomialHMM</span></code>, etc., all parameters must be passed to the
object when initialising it and not through <code class="docutils literal notranslate"><span class="pre">fit</span></code>. Now <code class="docutils literal notranslate"><span class="pre">fit</span></code> will
only accept the data as an input parameter.</p></li>
<li><p>For all SVM classes, a faulty behavior of <code class="docutils literal notranslate"><span class="pre">gamma</span></code> was fixed. Previously,
the default gamma value was only computed the first time <code class="docutils literal notranslate"><span class="pre">fit</span></code> was called
and then stored. It is now recalculated on every call to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p>All <code class="docutils literal notranslate"><span class="pre">Base</span></code> classes are now abstract meta classes so that they can not be
instantiated.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.cluster.ward_tree.html#sklearn.cluster.ward_tree" title="sklearn.cluster.ward_tree"><code class="xref py py-func docutils literal notranslate"><span class="pre">cluster.ward_tree</span></code></a> now also returns the parent array. This is
necessary for early-stopping in which case the tree is not
completely built.</p></li>
<li><p>In <a class="reference internal" href="../modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a> the parameters
<code class="docutils literal notranslate"><span class="pre">min_n</span></code> and <code class="docutils literal notranslate"><span class="pre">max_n</span></code> were joined to the parameter <code class="docutils literal notranslate"><span class="pre">n_gram_range</span></code> to
enable grid-searching both at once.</p></li>
<li><p>In <a class="reference internal" href="../modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" title="sklearn.feature_extraction.text.CountVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">CountVectorizer</span></code></a>, words that appear
only in one document are now ignored by default. To reproduce
the previous behavior, set <code class="docutils literal notranslate"><span class="pre">min_df=1</span></code>.</p></li>
<li><p>Fixed API inconsistency: <a class="reference internal" href="../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier.predict_proba" title="sklearn.linear_model.SGDClassifier.predict_proba"><code class="xref py py-meth docutils literal notranslate"><span class="pre">linear_model.SGDClassifier.predict_proba</span></code></a> now
returns 2d array when fit on two classes.</p></li>
<li><p>Fixed API inconsistency: <a class="reference internal" href="../modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function" title="sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function"><code class="xref py py-meth docutils literal notranslate"><span class="pre">discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function</span></code></a>
and <a class="reference internal" href="../modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function"><code class="xref py py-meth docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis.decision_function</span></code></a> now return 1d arrays
when fit on two classes.</p></li>
<li><p>Grid of alphas used for fitting <a class="reference internal" href="../modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV" title="sklearn.linear_model.LassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LassoCV</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">ElasticNetCV</span></code></a> is now stored
in the attribute <code class="docutils literal notranslate"><span class="pre">alphas_</span></code> rather than overriding the init parameter
<code class="docutils literal notranslate"><span class="pre">alphas</span></code>.</p></li>
<li><p>Linear models when alpha is estimated by cross-validation store
the estimated value in the <code class="docutils literal notranslate"><span class="pre">alpha_</span></code> attribute rather than just
<code class="docutils literal notranslate"><span class="pre">alpha</span></code> or <code class="docutils literal notranslate"><span class="pre">best_alpha</span></code>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingClassifier</span></code></a> now supports
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba" title="sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba"><code class="xref py py-meth docutils literal notranslate"><span class="pre">staged_predict_proba</span></code></a>, and
<a class="reference internal" href="../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier.staged_predict" title="sklearn.ensemble.GradientBoostingClassifier.staged_predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">staged_predict</span></code></a>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">svm.sparse.SVC</span></code> and other sparse SVM classes are now deprecated.
The all classes in the <a class="reference internal" href="../modules/svm.html#svm"><span class="std std-ref">Support Vector Machines</span></a> module now automatically select the
sparse or dense representation base on the input.</p></li>
<li><p>All clustering algorithms now interpret the array <code class="docutils literal notranslate"><span class="pre">X</span></code> given to <code class="docutils literal notranslate"><span class="pre">fit</span></code> as
input data, in particular <a class="reference internal" href="../modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" title="sklearn.cluster.SpectralClustering"><code class="xref py py-class docutils literal notranslate"><span class="pre">SpectralClustering</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" title="sklearn.cluster.AffinityPropagation"><code class="xref py py-class docutils literal notranslate"><span class="pre">AffinityPropagation</span></code></a> which previously expected affinity matrices.</p></li>
<li><p>For clustering algorithms that take the desired number of clusters as a parameter,
this parameter is now called <code class="docutils literal notranslate"><span class="pre">n_clusters</span></code>.</p></li>
</ul>
</section>
<section id="id2">
<h3>People<a class="headerlink" href="#id2" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>267 <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a></p></li>
<li><p>94 <a class="reference external" href="http://www.montefiore.ulg.ac.be/~glouppe/">Gilles Louppe</a></p></li>
<li><p>89 <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p>79 <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a></p></li>
<li><p>60 <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a></p></li>
<li><p>57 <a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a></p></li>
<li><p>52 <a class="reference external" href="https://vene.ro/">Vlad Niculae</a></p></li>
<li><p>45 <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a></p></li>
<li><p>44 Nelle Varoquaux</p></li>
<li><p>37 <a class="reference external" href="https://github.com/jaquesgrobler">Jaques Grobler</a></p></li>
<li><p>30 Alexis Mignon</p></li>
<li><p>30 Immanuel Bayer</p></li>
<li><p>27 <a class="reference external" href="https://bsky.app/profile/ogrisel.bsky.social">Olivier Grisel</a></p></li>
<li><p>16 Subhodeep Moitra</p></li>
<li><p>13 Yannick Schwartz</p></li>
<li><p>12 <a class="reference external" href="https://github.com/kernc">@kernc</a></p></li>
<li><p>11 <a class="reference external" href="https://github.com/VirgileFritsch">Virgile Fritsch</a></p></li>
<li><p>9 Daniel Duckworth</p></li>
<li><p>9 <a class="reference external" href="http://fa.bianp.net">Fabian Pedregosa</a></p></li>
<li><p>9 <a class="reference external" href="https://twitter.com/robertlayton">Robert Layton</a></p></li>
<li><p>8 John Benediktsson</p></li>
<li><p>7 Marko Burjek</p></li>
<li><p>5 <a class="reference external" href="https://twitter.com/npinto">Nicolas Pinto</a></p></li>
<li><p>4 Alexandre Abraham</p></li>
<li><p>4 <a class="reference external" href="https://staff.washington.edu/jakevdp/">Jake Vanderplas</a></p></li>
<li><p>3 <a class="reference external" href="http://personal.ee.surrey.ac.uk/Personal/B.Holt">Brian Holt</a></p></li>
<li><p>3 <a class="reference external" href="https://duchesnay.github.io/">Edouard Duchesnay</a></p></li>
<li><p>3 Florian Hoenig</p></li>
<li><p>3 flyingimmidev</p></li>
<li><p>2 Francois Savard</p></li>
<li><p>2 Hannes Schulz</p></li>
<li><p>2 Peter Welinder</p></li>
<li><p>2 <a class="reference external" href="http://www.onerussian.com/">Yaroslav Halchenko</a></p></li>
<li><p>2 Wei Li</p></li>
<li><p>1 Alex Companioni</p></li>
<li><p>1 Brandyn A. White</p></li>
<li><p>1 Bussonnier Matthias</p></li>
<li><p>1 Charles-Pierre Astolfi</p></li>
<li><p>1 Dan O’Huiginn</p></li>
<li><p>1 David Cournapeau</p></li>
<li><p>1 Keith Goodman</p></li>
<li><p>1 Ludwig Schwardt</p></li>
<li><p>1 Olivier Hervieu</p></li>
<li><p>1 Sergio Medina</p></li>
<li><p>1 Shiqiao Du</p></li>
<li><p>1 Tim Sheerman-Chase</p></li>
<li><p>1 buguen</p></li>
</ul>
</section>
</section>
<section id="version-0-11">
<span id="changes-0-11"></span><h2>Version 0.11<a class="headerlink" href="#version-0-11" title="Link to this heading">#</a></h2>
<p><strong>May 7, 2012</strong></p>
<section id="id3">
<h3>Changelog<a class="headerlink" href="#id3" title="Link to this heading">#</a></h3>
<section id="highlights">
<h4>Highlights<a class="headerlink" href="#highlights" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p>Gradient boosted regression trees (<a class="reference internal" href="../modules/ensemble.html#gradient-boosting"><span class="std std-ref">Gradient-boosted trees</span></a>)
for classification and regression by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a>
and <a class="reference external" href="https://twitter.com/scottblanc">Scott White</a> .</p></li>
<li><p>Simple dict-based feature loader with support for categorical variables
(<a class="reference internal" href="../modules/generated/sklearn.feature_extraction.DictVectorizer.html#sklearn.feature_extraction.DictVectorizer" title="sklearn.feature_extraction.DictVectorizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">DictVectorizer</span></code></a>) by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a>.</p></li>
<li><p>Added Matthews correlation coefficient (<a class="reference internal" href="../modules/generated/sklearn.metrics.matthews_corrcoef.html#sklearn.metrics.matthews_corrcoef" title="sklearn.metrics.matthews_corrcoef"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.matthews_corrcoef</span></code></a>)
and added macro and micro average options to
<a class="reference internal" href="../modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score" title="sklearn.metrics.precision_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">precision_score</span></code></a>, <a class="reference internal" href="../modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score" title="sklearn.metrics.recall_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.recall_score</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score" title="sklearn.metrics.f1_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">f1_score</span></code></a> by <a class="reference external" href="https://www.mit.edu/~satra/">Satrajit Ghosh</a>.</p></li>
<li><p><a class="reference internal" href="../modules/grid_search.html#out-of-bag"><span class="std std-ref">Out of Bag Estimates</span></a> of generalization error for <a class="reference internal" href="../modules/ensemble.html#ensemble"><span class="std std-ref">Ensembles: Gradient boosting, random forests, bagging, voting, stacking</span></a>
by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
<li><p>Randomized sparse linear models for feature
selection, by <a class="reference external" href="http://alexandre.gramfort.net">Alexandre Gramfort</a> and <a class="reference external" href="http://gael-varoquaux.info">Gael Varoquaux</a></p></li>
<li><p><a class="reference internal" href="../modules/semi_supervised.html#label-propagation"><span class="std std-ref">Label Propagation</span></a> for semi-supervised learning, by Clay
Woolam. <strong>Note</strong> the semi-supervised API is still work in progress,
and may change.</p></li>
<li><p>Added BIC/AIC model selection to classical <a class="reference internal" href="../modules/mixture.html#gmm"><span class="std std-ref">Gaussian mixture models</span></a> and unified
the API with the remainder of scikit-learn, by <a class="reference external" href="https://team.inria.fr/parietal/bertrand-thirions-page">Bertrand Thirion</a></p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">sklearn.cross_validation.StratifiedShuffleSplit</span></code>, which is
a <code class="docutils literal notranslate"><span class="pre">sklearn.cross_validation.ShuffleSplit</span></code> with balanced splits,
by Yannick Schwartz.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.neighbors.NearestCentroid.html#sklearn.neighbors.NearestCentroid" title="sklearn.neighbors.NearestCentroid"><code class="xref py py-class docutils literal notranslate"><span class="pre">NearestCentroid</span></code></a> classifier added, along with a
<code class="docutils literal notranslate"><span class="pre">shrink_threshold</span></code> parameter, which implements <strong>shrunken centroid
classification</strong>, by <a class="reference external" href="https://twitter.com/robertlayton">Robert Layton</a>.</p></li>
</ul>
</section>
<section id="other-changes">
<h4>Other changes<a class="headerlink" href="#other-changes" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p>Merged dense and sparse implementations of <a class="reference internal" href="../modules/sgd.html#sgd"><span class="std std-ref">Stochastic Gradient Descent</span></a> module and
exposed utility extension types for sequential
datasets <code class="docutils literal notranslate"><span class="pre">seq_dataset</span></code> and weight vectors <code class="docutils literal notranslate"><span class="pre">weight_vector</span></code>
by <a class="reference external" href="https://sites.google.com/site/peterprettenhofer/">Peter Prettenhofer</a>.</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> (support for online/minibatch learning) and
warm_start to the <a class="reference internal" href="../modules/sgd.html#sgd"><span class="std std-ref">Stochastic Gradient Descent</span></a> module by <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>.</p></li>
<li><p>Dense and sparse implementations of <a class="reference internal" href="../modules/svm.html#svm"><span class="std std-ref">Support Vector Machines</span></a> classes and
<a class="reference internal" href="../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a> merged by <a class="reference external" href="https://github.com/larsmans">Lars Buitinck</a>.</p></li>
<li><p>Regressors can now be used as base estimator in the <a class="reference internal" href="../modules/multiclass.html#multiclass"><span class="std std-ref">Multiclass and multioutput algorithms</span></a>
module by <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>.</p></li>
<li><p>Added n_jobs option to <a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.pairwise_distances</span></code></a>
and <a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html#sklearn.metrics.pairwise.pairwise_kernels" title="sklearn.metrics.pairwise.pairwise_kernels"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.pairwise.pairwise_kernels</span></code></a> for parallel computation,
by <a class="reference external" href="http://www.mblondel.org">Mathieu Blondel</a>.</p></li>
<li><p><a class="reference internal" href="../modules/clustering.html#k-means"><span class="std std-ref">K-means</span></a> can now be run in parallel, using the <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> argument
to either <a class="reference internal" href="../modules/clustering.html#k-means"><span class="std std-ref">K-means</span></a> or <a class="reference internal" href="../modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-class docutils literal notranslate"><span class="pre">cluster.KMeans</span></code></a>, by <a class="reference external" href="https://twitter.com/robertlayton">Robert Layton</a>.</p></li>
<li><p>Improved <a class="reference internal" href="../modules/cross_validation.html#cross-validation"><span class="std std-ref">Cross-validation: evaluating estimator performance</span></a> and <a class="reference internal" href="../modules/grid_search.html#grid-search"><span class="std std-ref">Tuning the hyper-parameters of an estimator</span></a> documentation
and introduced the new <code class="docutils literal notranslate"><span class="pre">cross_validation.train_test_split</span></code>
helper function by <a class="reference external" href="https://bsky.app/profile/ogrisel.bsky.social">Olivier Grisel</a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">SVC</span></code></a> members <code class="docutils literal notranslate"><span class="pre">coef_</span></code> and <code class="docutils literal notranslate"><span class="pre">intercept_</span></code> changed sign for
consistency with <code class="docutils literal notranslate"><span class="pre">decision_function</span></code>; for <code class="docutils literal notranslate"><span class="pre">kernel==linear</span></code>,
<code class="docutils literal notranslate"><span class="pre">coef_</span></code> was fixed in the one-vs-one case, by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
<li><p>Performance improvements to efficient leave-one-out cross-validated
Ridge regression, esp. for the <code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre">></span> <span class="pre">n_features</span></code> case, in
<a class="reference internal" href="../modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">RidgeCV</span></code></a>, by Reuben Fletcher-Costin.</p></li>
<li><p>Refactoring and simplification of the <a class="reference internal" href="../modules/feature_extraction.html#text-feature-extraction"><span class="std std-ref">Text feature extraction</span></a>
API and fixed a bug that caused possible negative IDF,
by <a class="reference external" href="https://bsky.app/profile/ogrisel.bsky.social">Olivier Grisel</a>.</p></li>
<li><p>Beam pruning option in <code class="docutils literal notranslate"><span class="pre">_BaseHMM</span></code> module has been removed since it
is difficult to Cythonize. If you are interested in contributing a Cython
version, you can use the python version in the git history as a reference.</p></li>
<li><p>Classes in <a class="reference internal" href="../modules/neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a> now support arbitrary Minkowski metric for
nearest neighbors searches. The metric can be specified by argument <code class="docutils literal notranslate"><span class="pre">p</span></code>.</p></li>
</ul>
</section>
</section>
<section id="id4">
<h3>API changes summary<a class="headerlink" href="#id4" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">covariance.EllipticEnvelop</span></code> is now deprecated.
Please use <a class="reference internal" href="../modules/generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope" title="sklearn.covariance.EllipticEnvelope"><code class="xref py py-class docutils literal notranslate"><span class="pre">EllipticEnvelope</span></code></a> instead.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">NeighborsClassifier</span></code> and <code class="docutils literal notranslate"><span class="pre">NeighborsRegressor</span></code> are gone in the module
<a class="reference internal" href="../modules/neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a>. Use the classes <a class="reference internal" href="../modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="sklearn.neighbors.KNeighborsClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">KNeighborsClassifier</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html#sklearn.neighbors.RadiusNeighborsClassifier" title="sklearn.neighbors.RadiusNeighborsClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RadiusNeighborsClassifier</span></code></a>, <a class="reference internal" href="../modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor" title="sklearn.neighbors.KNeighborsRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">KNeighborsRegressor</span></code></a>
and/or <a class="reference internal" href="../modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor" title="sklearn.neighbors.RadiusNeighborsRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">RadiusNeighborsRegressor</span></code></a> instead.</p></li>
<li><p>Sparse classes in the <a class="reference internal" href="../modules/sgd.html#sgd"><span class="std std-ref">Stochastic Gradient Descent</span></a> module are now deprecated.</p></li>
<li><p>In <code class="docutils literal notranslate"><span class="pre">mixture.GMM</span></code>, <code class="docutils literal notranslate"><span class="pre">mixture.DPGMM</span></code> and <code class="docutils literal notranslate"><span class="pre">mixture.VBGMM</span></code>,
parameters must be passed to an object when initialising it and not through
<code class="docutils literal notranslate"><span class="pre">fit</span></code>. Now <code class="docutils literal notranslate"><span class="pre">fit</span></code> will only accept the data as an input parameter.</p></li>
<li><p>methods <code class="docutils literal notranslate"><span class="pre">rvs</span></code> and <code class="docutils literal notranslate"><span class="pre">decode</span></code> in <code class="docutils literal notranslate"><span class="pre">GMM</span></code> module are now deprecated.
<code class="docutils literal notranslate"><span class="pre">sample</span></code> and <code class="docutils literal notranslate"><span class="pre">score</span></code> or <code class="docutils literal notranslate"><span class="pre">predict</span></code> should be used instead.</p></li>
<li><p>attribute <code class="docutils literal notranslate"><span class="pre">_scores</span></code> and <code class="docutils literal notranslate"><span class="pre">_pvalues</span></code> in univariate feature selection
objects are now deprecated.
<code class="docutils literal notranslate"><span class="pre">scores_</span></code> or <code class="docutils literal notranslate"><span class="pre">pvalues_</span></code> should be used instead.</p></li>
<li><p>In <a class="reference internal" href="../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>, <a class="reference internal" href="../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearSVC</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">SVC</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC" title="sklearn.svm.NuSVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">NuSVC</span></code></a>, the <code class="docutils literal notranslate"><span class="pre">class_weight</span></code> parameter is
now an initialization parameter, not a parameter to fit. This makes grid
searches over this parameter possible.</p></li>
<li><p>LFW <code class="docutils literal notranslate"><span class="pre">data</span></code> is now always shape <code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_features)</span></code> to be
consistent with the Olivetti faces dataset. Use <code class="docutils literal notranslate"><span class="pre">images</span></code> and
<code class="docutils literal notranslate"><span class="pre">pairs</span></code> attribute to access the natural images shapes instead.</p></li>
<li><p>In <a class="reference internal" href="../modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearSVC</span></code></a>, the meaning of the <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> parameter
changed. Options now are <code class="docutils literal notranslate"><span class="pre">'ovr'</span></code> and <code class="docutils literal notranslate"><span class="pre">'crammer_singer'</span></code>, with
<code class="docutils literal notranslate"><span class="pre">'ovr'</span></code> being the default. This does not change the default behavior
but hopefully is less confusing.</p></li>
<li><p>Class <code class="docutils literal notranslate"><span class="pre">feature_selection.text.Vectorizer</span></code> is deprecated and
replaced by <code class="docutils literal notranslate"><span class="pre">feature_selection.text.TfidfVectorizer</span></code>.</p></li>
<li><p>The preprocessor / analyzer nested structure for text feature
extraction has been removed. All those features are
now directly passed as flat constructor arguments
to <code class="docutils literal notranslate"><span class="pre">feature_selection.text.TfidfVectorizer</span></code> and
<code class="docutils literal notranslate"><span class="pre">feature_selection.text.CountVectorizer</span></code>, in particular the
following parameters are now used:</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">analyzer</span></code> can be <code class="docutils literal notranslate"><span class="pre">'word'</span></code> or <code class="docutils literal notranslate"><span class="pre">'char'</span></code> to switch the default
analysis scheme, or use a specific python callable (as previously).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">tokenizer</span></code> and <code class="docutils literal notranslate"><span class="pre">preprocessor</span></code> have been introduced to make it
still possible to customize those steps with the new API.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">input</span></code> explicitly control how to interpret the sequence passed to
<code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">predict</span></code>: filenames, file objects or direct (byte or
Unicode) strings.</p></li>
<li><p>charset decoding is explicit and strict by default.</p></li>
<li><p>the <code class="docutils literal notranslate"><span class="pre">vocabulary</span></code>, fitted or not is now stored in the
<code class="docutils literal notranslate"><span class="pre">vocabulary_</span></code> attribute to be consistent with the project
conventions.</p></li>
<li><p>Class <code class="docutils literal notranslate"><span class="pre">feature_selection.text.TfidfVectorizer</span></code> now derives directly
from <code class="docutils literal notranslate"><span class="pre">feature_selection.text.CountVectorizer</span></code> to make grid
search trivial.</p></li>
<li><p>methods <code class="docutils literal notranslate"><span class="pre">rvs</span></code> in <code class="docutils literal notranslate"><span class="pre">_BaseHMM</span></code> module are now deprecated.
<code class="docutils literal notranslate"><span class="pre">sample</span></code> should be used instead.</p></li>
<li><p>Beam pruning option in <code class="docutils literal notranslate"><span class="pre">_BaseHMM</span></code> module is removed since it is
difficult to be Cythonized. If you are interested, you can look in the
history codes by git.</p></li>
<li><p>The SVMlight format loader now supports files with both zero-based and
one-based column indices, since both occur “in the wild”.</p></li>
<li><p>Arguments in class <a class="reference internal" href="../modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit" title="sklearn.model_selection.ShuffleSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShuffleSplit</span></code></a> are now consistent with
<a class="reference internal" href="../modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="sklearn.model_selection.StratifiedShuffleSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedShuffleSplit</span></code></a>. Arguments <code class="docutils literal notranslate"><span class="pre">test_fraction</span></code> and
<code class="docutils literal notranslate"><span class="pre">train_fraction</span></code> are deprecated and renamed to <code class="docutils literal notranslate"><span class="pre">test_size</span></code> and
<code class="docutils literal notranslate"><span class="pre">train_size</span></code> and can accept both <code class="docutils literal notranslate"><span class="pre">float</span></code> and <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p></li>
<li><p>Arguments in class <code class="docutils literal notranslate"><span class="pre">Bootstrap</span></code> are now consistent with
<a class="reference internal" href="../modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="sklearn.model_selection.StratifiedShuffleSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedShuffleSplit</span></code></a>. Arguments <code class="docutils literal notranslate"><span class="pre">n_test</span></code> and
<code class="docutils literal notranslate"><span class="pre">n_train</span></code> are deprecated and renamed to <code class="docutils literal notranslate"><span class="pre">test_size</span></code> and
<code class="docutils literal notranslate"><span class="pre">train_size</span></code> and can accept both <code class="docutils literal notranslate"><span class="pre">float</span></code> and <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p></li>
<li><p>Argument <code class="docutils literal notranslate"><span class="pre">p</span></code> added to classes in <a class="reference internal" href="../modules/neighbors.html#neighbors"><span class="std std-ref">Nearest Neighbors</span></a> to specify an
arbitrary Minkowski metric for nearest neighbors searches.</p></li>
</ul>
</section>
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