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<section id="version-0-18">
<h1>Version 0.18<a class="headerlink" href="#version-0-18" title="Link to this heading">#</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Scikit-learn 0.18 is the last major release of scikit-learn to support Python 2.6.
Later versions of scikit-learn will require Python 2.7 or above.</p>
</div>
<section id="version-0-18-2">
<span id="changes-0-18-2"></span><h2>Version 0.18.2<a class="headerlink" href="#version-0-18-2" title="Link to this heading">#</a></h2>
<p><strong>June 20, 2017</strong></p>
<section id="changelog">
<h3>Changelog<a class="headerlink" href="#changelog" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p>Fixes for compatibility with NumPy 1.13.0: <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7946">#7946</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/8355">#8355</a> by
<a class="reference external" href="https://github.com/lesteve">Loic Esteve</a>.</p></li>
<li><p>Minor compatibility changes in the examples <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/9010">#9010</a> <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/8040">#8040</a>
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/9149">#9149</a>.</p></li>
</ul>
</section>
<section id="code-contributors">
<h3>Code Contributors<a class="headerlink" href="#code-contributors" title="Link to this heading">#</a></h3>
<p>Aman Dalmia, Loic Esteve, Nate Guerin, Sergei Lebedev</p>
</section>
</section>
<section id="version-0-18-1">
<span id="changes-0-18-1"></span><h2>Version 0.18.1<a class="headerlink" href="#version-0-18-1" title="Link to this heading">#</a></h2>
<p><strong>November 11, 2016</strong></p>
<section id="id1">
<h3>Changelog<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
<section id="enhancements">
<h4>Enhancements<a class="headerlink" href="#enhancements" title="Link to this heading">#</a></h4>
<ul>
<li><p>Improved <code class="docutils literal notranslate"><span class="pre">sample_without_replacement</span></code> speed by utilizing
numpy.random.permutation for most cases. As a result,
samples may differ in this release for a fixed random state.
Affected estimators:</p>
<ul class="simple">
<li><p><a class="reference internal" href="../modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.BaggingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.BaggingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.RANSACRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.RandomizedSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.random_projection.SparseRandomProjection.html#sklearn.random_projection.SparseRandomProjection" title="sklearn.random_projection.SparseRandomProjection"><code class="xref py py-class docutils literal notranslate"><span class="pre">random_projection.SparseRandomProjection</span></code></a></p></li>
</ul>
<p>This also affects the <a class="reference internal" href="../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-meth docutils literal notranslate"><span class="pre">datasets.make_classification</span></code></a>
method.</p>
</li>
</ul>
</section>
<section id="bug-fixes">
<h4>Bug fixes<a class="headerlink" href="#bug-fixes" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p>Fix issue where <code class="docutils literal notranslate"><span class="pre">min_grad_norm</span></code> and <code class="docutils literal notranslate"><span class="pre">n_iter_without_progress</span></code>
parameters were not being utilised by <a class="reference internal" href="../modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE" title="sklearn.manifold.TSNE"><code class="xref py py-class docutils literal notranslate"><span class="pre">manifold.TSNE</span></code></a>.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6497">#6497</a> by <a class="reference external" href="https://github.com/ssaeger">Sebastian Säger</a></p></li>
<li><p>Fix bug for svm’s decision values when <code class="docutils literal notranslate"><span class="pre">decision_function_shape</span></code>
is <code class="docutils literal notranslate"><span class="pre">ovr</span></code> in <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">svm.SVC</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">svm.SVC</span></code></a>’s decision_function was incorrect from versions
0.17.0 through 0.18.0.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7724">#7724</a> by <a class="reference external" href="https://github.com/btdai">Bing Tian Dai</a></p></li>
<li><p>Attribute <code class="docutils literal notranslate"><span class="pre">explained_variance_ratio</span></code> of
<a class="reference internal" href="../modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a> calculated
with SVD and Eigen solver are now of the same length. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7632">#7632</a>
by <a class="reference external" href="https://github.com/JPFrancoia">JPFrancoia</a></p></li>
<li><p>Fixes issue in <a class="reference internal" href="../modules/feature_selection.html#univariate-feature-selection"><span class="std std-ref">Univariate feature selection</span></a> where score
functions were not accepting multi-label targets. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7676">#7676</a>
by <a class="reference external" href="https://github.com/affanv14">Mohammed Affan</a></p></li>
<li><p>Fixed setting parameters when calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> multiple times on
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel" title="sklearn.feature_selection.SelectFromModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">feature_selection.SelectFromModel</span></code></a>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7756">#7756</a> by <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a></p></li>
<li><p>Fixes issue in <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method of
<a class="reference internal" href="../modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">multiclass.OneVsRestClassifier</span></code></a> when number of classes used in
<code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> was less than the total number of classes in the
data. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7786">#7786</a> by <a class="reference external" href="https://github.com/srivatsan-ramesh">Srivatsan Ramesh</a></p></li>
<li><p>Fixes issue in <a class="reference internal" href="../modules/generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">calibration.CalibratedClassifierCV</span></code></a> where
the sum of probabilities of each class for a data was not 1, and
<code class="docutils literal notranslate"><span class="pre">CalibratedClassifierCV</span></code> now handles the case where the training set
has less number of classes than the total data. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7799">#7799</a> by
<a class="reference external" href="https://github.com/srivatsan-ramesh">Srivatsan Ramesh</a></p></li>
<li><p>Fix a bug where <a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectFdr.html#sklearn.feature_selection.SelectFdr" title="sklearn.feature_selection.SelectFdr"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.SelectFdr</span></code></a> did not
exactly implement Benjamini-Hochberg procedure. It formerly may have
selected fewer features than it should.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7490">#7490</a> by <a class="reference external" href="https://github.com/mpjlu">Peng Meng</a>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.manifold.LocallyLinearEmbedding.html#sklearn.manifold.LocallyLinearEmbedding" title="sklearn.manifold.LocallyLinearEmbedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.manifold.LocallyLinearEmbedding</span></code></a> now correctly handles
integer inputs. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6282">#6282</a> by <a class="reference external" href="https://staff.washington.edu/jakevdp/">Jake Vanderplas</a>.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">min_weight_fraction_leaf</span></code> parameter of tree-based classifiers and
regressors now assumes uniform sample weights by default if the
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> argument is not passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> function.
Previously, the parameter was silently ignored. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7301">#7301</a>
by <a class="reference external" href="https://github.com/nelson-liu">Nelson Liu</a>.</p></li>
<li><p>Numerical issue with <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">linear_model.RidgeCV</span></code></a> on centered data when
<code class="docutils literal notranslate"><span class="pre">n_features</span> <span class="pre">></span> <span class="pre">n_samples</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6178">#6178</a> by <a class="reference external" href="https://team.inria.fr/parietal/bertrand-thirions-page">Bertrand Thirion</a></p></li>
<li><p>Tree splitting criterion classes’ cloning/pickling is now memory safe
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7680">#7680</a> by <a class="reference external" href="https://github.com/olologin">Ibraim Ganiev</a>.</p></li>
<li><p>Fixed a bug where <a class="reference internal" href="../modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF" title="sklearn.decomposition.NMF"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.NMF</span></code></a> sets its <code class="docutils literal notranslate"><span class="pre">n_iters_</span></code>
attribute in <code class="docutils literal notranslate"><span class="pre">transform()</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7553">#7553</a> by <a class="reference external" href="https://github.com/kiote">Ekaterina
Krivich</a>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegressionCV</span></code></a> now correctly handles
string labels. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/5874">#5874</a> by <a class="reference external" href="https://github.com/raghavrv">Raghav RV</a>.</p></li>
<li><p>Fixed a bug where <a class="reference internal" href="../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.train_test_split</span></code></a> raised
an error when <code class="docutils literal notranslate"><span class="pre">stratify</span></code> is a list of string labels. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7593">#7593</a> by
<a class="reference external" href="https://github.com/raghavrv">Raghav RV</a>.</p></li>
<li><p>Fixed a bug where <a class="reference internal" href="../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.GridSearchCV</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.RandomizedSearchCV</span></code></a> were not pickleable
because of a pickling bug in <code class="docutils literal notranslate"><span class="pre">np.ma.MaskedArray</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7594">#7594</a> by
<a class="reference external" href="https://github.com/raghavrv">Raghav RV</a>.</p></li>
<li><p>All cross-validation utilities in <a class="reference internal" href="../api/sklearn.model_selection.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a> now
permit one time cross-validation splitters for the <code class="docutils literal notranslate"><span class="pre">cv</span></code> parameter. Also
non-deterministic cross-validation splitters (where multiple calls to
<code class="docutils literal notranslate"><span class="pre">split</span></code> produce dissimilar splits) can be used as <code class="docutils literal notranslate"><span class="pre">cv</span></code> parameter.
The <a class="reference internal" href="../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.GridSearchCV</span></code></a> will cross-validate each
parameter setting on the split produced by the first <code class="docutils literal notranslate"><span class="pre">split</span></code> call
to the cross-validation splitter. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7660">#7660</a> by <a class="reference external" href="https://github.com/raghavrv">Raghav RV</a>.</p></li>
<li><p>Fix bug where <a class="reference internal" href="../modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html#sklearn.preprocessing.MultiLabelBinarizer.fit_transform" title="sklearn.preprocessing.MultiLabelBinarizer.fit_transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">preprocessing.MultiLabelBinarizer.fit_transform</span></code></a>
returned an invalid CSR matrix.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7750">#7750</a> by <a class="reference external" href="https://github.com/perimosocordiae">CJ Carey</a>.</p></li>
<li><p>Fixed a bug where <a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.cosine_distances.html#sklearn.metrics.pairwise.cosine_distances" title="sklearn.metrics.pairwise.cosine_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.pairwise.cosine_distances</span></code></a> could return a
small negative distance. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7732">#7732</a> by <a class="reference external" href="https://github.com/asanakoy">Artsion</a>.</p></li>
</ul>
</section>
</section>
<section id="api-changes-summary">
<h3>API changes summary<a class="headerlink" href="#api-changes-summary" title="Link to this heading">#</a></h3>
<p>Trees and forests</p>
<ul class="simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">min_weight_fraction_leaf</span></code> parameter of tree-based classifiers and
regressors now assumes uniform sample weights by default if the
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> argument is not passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> function.
Previously, the parameter was silently ignored. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7301">#7301</a> by <a class="reference external" href="https://github.com/nelson-liu">Nelson
Liu</a>.</p></li>
<li><p>Tree splitting criterion classes’ cloning/pickling is now memory safe.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7680">#7680</a> by <a class="reference external" href="https://github.com/olologin">Ibraim Ganiev</a>.</p></li>
</ul>
<p>Linear, kernelized and related models</p>
<ul class="simple">
<li><p>Length of <code class="docutils literal notranslate"><span class="pre">explained_variance_ratio</span></code> of
<a class="reference internal" href="../modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a>
changed for both Eigen and SVD solvers. The attribute has now a length
of min(n_components, n_classes - 1). <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7632">#7632</a>
by <a class="reference external" href="https://github.com/JPFrancoia">JPFrancoia</a></p></li>
<li><p>Numerical issue with <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">linear_model.RidgeCV</span></code></a> on centered data when
<code class="docutils literal notranslate"><span class="pre">n_features</span> <span class="pre">></span> <span class="pre">n_samples</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6178">#6178</a> by <a class="reference external" href="https://team.inria.fr/parietal/bertrand-thirions-page">Bertrand Thirion</a></p></li>
</ul>
</section>
</section>
<section id="changes-0-18">
<span id="id2"></span><h2>Version 0.18<a class="headerlink" href="#changes-0-18" title="Link to this heading">#</a></h2>
<p><strong>September 28, 2016</strong></p>
<section id="model-selection-enhancements-and-api-changes">
<span id="model-selection-changes"></span><h3>Model Selection Enhancements and API Changes<a class="headerlink" href="#model-selection-enhancements-and-api-changes" title="Link to this heading">#</a></h3>
<ul>
<li><p><strong>The model_selection module</strong></p>
<p>The new module <a class="reference internal" href="../api/sklearn.model_selection.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a>, which groups together the
functionalities of formerly <code class="docutils literal notranslate"><span class="pre">sklearn.cross_validation</span></code>,
<code class="docutils literal notranslate"><span class="pre">sklearn.grid_search</span></code> and <code class="docutils literal notranslate"><span class="pre">sklearn.learning_curve</span></code>, introduces new
possibilities such as nested cross-validation and better manipulation of
parameter searches with Pandas.</p>
<p>Many things will stay the same but there are some key differences. Read
below to know more about the changes.</p>
</li>
<li><p><strong>Data-independent CV splitters enabling nested cross-validation</strong></p>
<p>The new cross-validation splitters, defined in the
<a class="reference internal" href="../api/sklearn.model_selection.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a>, are no longer initialized with any
data-dependent parameters such as <code class="docutils literal notranslate"><span class="pre">y</span></code>. Instead they expose a
<code class="docutils literal notranslate"><span class="pre">split</span></code> method that takes in the data and yields a generator for the
different splits.</p>
<p>This change makes it possible to use the cross-validation splitters to
perform nested cross-validation, facilitated by
<a class="reference internal" href="../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GridSearchCV</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.RandomizedSearchCV</span></code></a> utilities.</p>
</li>
<li><p><strong>The enhanced cv_results_ attribute</strong></p>
<p>The new <code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> attribute (of <a class="reference internal" href="../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GridSearchCV</span></code></a>
and <a class="reference internal" href="../modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.RandomizedSearchCV</span></code></a>) introduced in lieu of the
<code class="docutils literal notranslate"><span class="pre">grid_scores_</span></code> attribute is a dict of 1D arrays with elements in each
array corresponding to the parameter settings (i.e. search candidates).</p>
<p>The <code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> dict can be easily imported into <code class="docutils literal notranslate"><span class="pre">pandas</span></code> as a
<code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> for exploring the search results.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> arrays include scores for each cross-validation split
(with keys such as <code class="docutils literal notranslate"><span class="pre">'split0_test_score'</span></code>), as well as their mean
(<code class="docutils literal notranslate"><span class="pre">'mean_test_score'</span></code>) and standard deviation (<code class="docutils literal notranslate"><span class="pre">'std_test_score'</span></code>).</p>
<p>The ranks for the search candidates (based on their mean
cross-validation score) is available at <code class="docutils literal notranslate"><span class="pre">cv_results_['rank_test_score']</span></code>.</p>
<p>The parameter values for each parameter is stored separately as numpy
masked object arrays. The value, for that search candidate, is masked if
the corresponding parameter is not applicable. Additionally a list of all
the parameter dicts are stored at <code class="docutils literal notranslate"><span class="pre">cv_results_['params']</span></code>.</p>
</li>
<li><p><strong>Parameters n_folds and n_iter renamed to n_splits</strong></p>
<p>Some parameter names have changed:
The <code class="docutils literal notranslate"><span class="pre">n_folds</span></code> parameter in new <a class="reference internal" href="../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.KFold</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GroupKFold</span></code></a> (see below for the name change),
and <a class="reference internal" href="../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="sklearn.model_selection.StratifiedKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.StratifiedKFold</span></code></a> is now renamed to
<code class="docutils literal notranslate"><span class="pre">n_splits</span></code>. The <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> parameter in
<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">model_selection.ShuffleSplit</span></code></a>, the new class
<a class="reference internal" href="../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection.GroupShuffleSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GroupShuffleSplit</span></code></a> and
<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">model_selection.StratifiedShuffleSplit</span></code></a> is now renamed to
<code class="docutils literal notranslate"><span class="pre">n_splits</span></code>.</p>
</li>
<li><p><strong>Rename of splitter classes which accepts group labels along with data</strong></p>
<p>The cross-validation splitters <code class="docutils literal notranslate"><span class="pre">LabelKFold</span></code>,
<code class="docutils literal notranslate"><span class="pre">LabelShuffleSplit</span></code>, <code class="docutils literal notranslate"><span class="pre">LeaveOneLabelOut</span></code> and <code class="docutils literal notranslate"><span class="pre">LeavePLabelOut</span></code> have
been renamed to <a class="reference internal" href="../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GroupKFold</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection.GroupShuffleSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GroupShuffleSplit</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.LeaveOneGroupOut.html#sklearn.model_selection.LeaveOneGroupOut" title="sklearn.model_selection.LeaveOneGroupOut"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.LeaveOneGroupOut</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.model_selection.LeavePGroupsOut.html#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection.LeavePGroupsOut"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.LeavePGroupsOut</span></code></a> respectively.</p>
<p>Note the change from singular to plural form in
<a class="reference internal" href="../modules/generated/sklearn.model_selection.LeavePGroupsOut.html#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection.LeavePGroupsOut"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.LeavePGroupsOut</span></code></a>.</p>
</li>
<li><p><strong>Fit parameter labels renamed to groups</strong></p>
<p>The <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in the <code class="docutils literal notranslate"><span class="pre">split</span></code> method of the newly renamed
splitters <a class="reference internal" href="../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GroupKFold</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.LeaveOneGroupOut.html#sklearn.model_selection.LeaveOneGroupOut" title="sklearn.model_selection.LeaveOneGroupOut"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.LeaveOneGroupOut</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.LeavePGroupsOut.html#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection.LeavePGroupsOut"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.LeavePGroupsOut</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="sklearn.model_selection.GroupShuffleSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GroupShuffleSplit</span></code></a> is renamed to <code class="docutils literal notranslate"><span class="pre">groups</span></code>
following the new nomenclature of their class names.</p>
</li>
<li><p><strong>Parameter n_labels renamed to n_groups</strong></p>
<p>The parameter <code class="docutils literal notranslate"><span class="pre">n_labels</span></code> in the newly renamed
<a class="reference internal" href="../modules/generated/sklearn.model_selection.LeavePGroupsOut.html#sklearn.model_selection.LeavePGroupsOut" title="sklearn.model_selection.LeavePGroupsOut"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.LeavePGroupsOut</span></code></a> is changed to <code class="docutils literal notranslate"><span class="pre">n_groups</span></code>.</p>
</li>
<li><p>Training scores and Timing information</p>
<p><code class="docutils literal notranslate"><span class="pre">cv_results_</span></code> also includes the training scores for each
cross-validation split (with keys such as <code class="docutils literal notranslate"><span class="pre">'split0_train_score'</span></code>), as
well as their mean (<code class="docutils literal notranslate"><span class="pre">'mean_train_score'</span></code>) and standard deviation
(<code class="docutils literal notranslate"><span class="pre">'std_train_score'</span></code>). To avoid the cost of evaluating training score,
set <code class="docutils literal notranslate"><span class="pre">return_train_score=False</span></code>.</p>
<p>Additionally the mean and standard deviation of the times taken to split,
train and score the model across all the cross-validation splits is
available at the key <code class="docutils literal notranslate"><span class="pre">'mean_time'</span></code> and <code class="docutils literal notranslate"><span class="pre">'std_time'</span></code> respectively.</p>
</li>
</ul>
</section>
<section id="id3">
<h3>Changelog<a class="headerlink" href="#id3" title="Link to this heading">#</a></h3>
<section id="new-features">
<h4>New features<a class="headerlink" href="#new-features" title="Link to this heading">#</a></h4>
<p>Classifiers and Regressors</p>
<ul class="simple">
<li><p>The Gaussian Process module has been reimplemented and now offers classification
and regression estimators through <a class="reference internal" href="../modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier" title="sklearn.gaussian_process.GaussianProcessClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">gaussian_process.GaussianProcessClassifier</span></code></a>
and <a class="reference internal" href="../modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor" title="sklearn.gaussian_process.GaussianProcessRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">gaussian_process.GaussianProcessRegressor</span></code></a>. Among other things, the new
implementation supports kernel engineering, gradient-based hyperparameter optimization or
sampling of functions from GP prior and GP posterior. Extensive documentation and
examples are provided. By <a class="reference external" href="https://jmetzen.github.io/">Jan Hendrik Metzen</a>.</p></li>
<li><p>Added new supervised learning algorithm: <a class="reference internal" href="../modules/neural_networks_supervised.html#multilayer-perceptron"><span class="std std-ref">Multi-layer Perceptron</span></a>
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/3204">#3204</a> by <a class="reference external" href="https://github.com/IssamLaradji">Issam H. Laradji</a></p></li>
<li><p>Added <a class="reference internal" href="../modules/generated/sklearn.linear_model.HuberRegressor.html#sklearn.linear_model.HuberRegressor" title="sklearn.linear_model.HuberRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.HuberRegressor</span></code></a>, a linear model robust to outliers.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/5291">#5291</a> by <a class="reference external" href="https://manojbits.wordpress.com">Manoj Kumar</a>.</p></li>
<li><p>Added the <a class="reference internal" href="../modules/generated/sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">multioutput.MultiOutputRegressor</span></code></a> meta-estimator. It
converts single output regressors to multi-output regressors by fitting
one regressor per output. By <a class="reference external" href="https://github.com/betatim">Tim Head</a>.</p></li>
</ul>
<p>Other estimators</p>
<ul class="simple">
<li><p>New <a class="reference internal" href="../modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture" title="sklearn.mixture.GaussianMixture"><code class="xref py py-class docutils literal notranslate"><span class="pre">mixture.GaussianMixture</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.mixture.BayesianGaussianMixture.html#sklearn.mixture.BayesianGaussianMixture" title="sklearn.mixture.BayesianGaussianMixture"><code class="xref py py-class docutils literal notranslate"><span class="pre">mixture.BayesianGaussianMixture</span></code></a>
replace former mixture models, employing faster inference
for sounder results. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/7295">#7295</a> by <a class="reference external" href="https://github.com/xuewei4d">Wei Xue</a> and
<a class="reference external" href="https://github.com/tguillemot">Thierry Guillemot</a>.</p></li>
<li><p>Class <code class="docutils literal notranslate"><span class="pre">decomposition.RandomizedPCA</span></code> is now factored into <a class="reference internal" href="../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a>
and it is available calling with parameter <code class="docutils literal notranslate"><span class="pre">svd_solver='randomized'</span></code>.
The default number of <code class="docutils literal notranslate"><span class="pre">n_iter</span></code> for <code class="docutils literal notranslate"><span class="pre">'randomized'</span></code> has changed to 4. The old
behavior of PCA is recovered by <code class="docutils literal notranslate"><span class="pre">svd_solver='full'</span></code>. An additional solver
calls <code class="docutils literal notranslate"><span class="pre">arpack</span></code> and performs truncated (non-randomized) SVD. By default,
the best solver is selected depending on the size of the input and the
number of components requested. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/5299">#5299</a> by <a class="reference external" href="https://github.com/giorgiop">Giorgio Patrini</a>.</p></li>
<li><p>Added two functions for mutual information estimation:
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.mutual_info_classif.html#sklearn.feature_selection.mutual_info_classif" title="sklearn.feature_selection.mutual_info_classif"><code class="xref py py-func docutils literal notranslate"><span class="pre">feature_selection.mutual_info_classif</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.mutual_info_regression.html#sklearn.feature_selection.mutual_info_regression" title="sklearn.feature_selection.mutual_info_regression"><code class="xref py py-func docutils literal notranslate"><span class="pre">feature_selection.mutual_info_regression</span></code></a>. These functions can be
used in <a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest"><code class="xref py py-class docutils literal notranslate"><span class="pre">feature_selection.SelectKBest</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectPercentile.html#sklearn.feature_selection.SelectPercentile" title="sklearn.feature_selection.SelectPercentile"><code class="xref py py-class docutils literal notranslate"><span class="pre">feature_selection.SelectPercentile</span></code></a> as score functions.
By <a class="reference external" href="https://github.com/AndreaBravi">Andrea Bravi</a> and <a class="reference external" href="https://github.com/nmayorov">Nikolay Mayorov</a>.</p></li>
<li><p>Added the <a class="reference internal" href="../modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest" title="sklearn.ensemble.IsolationForest"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.IsolationForest</span></code></a> class for anomaly detection based on
random forests. By <a class="reference external" href="https://ngoix.github.io/">Nicolas Goix</a>.</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">algorithm="elkan"</span></code> to <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> implementing
Elkan’s fast K-Means algorithm. By <a class="reference external" href="https://amueller.github.io/">Andreas Müller</a>.</p></li>
</ul>
<p>Model selection and evaluation</p>
<ul class="simple">
<li><p>Added <a class="reference internal" href="../modules/generated/sklearn.metrics.fowlkes_mallows_score.html#sklearn.metrics.fowlkes_mallows_score" title="sklearn.metrics.fowlkes_mallows_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.fowlkes_mallows_score</span></code></a>, the Fowlkes Mallows
Index which measures the similarity of two clusterings of a set of points
By <a class="reference external" href="https://github.com/afouchet">Arnaud Fouchet</a> and <a class="reference external" href="https://github.com/tguillemot">Thierry Guillemot</a>.</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">metrics.calinski_harabaz_score</span></code>, which computes the Calinski
and Harabaz score to evaluate the resulting clustering of a set of points.
By <a class="reference external" href="https://github.com/afouchet">Arnaud Fouchet</a> and <a class="reference external" href="https://github.com/tguillemot">Thierry Guillemot</a>.</p></li>
<li><p>Added new cross-validation splitter
<a class="reference internal" href="../modules/generated/sklearn.model_selection.TimeSeriesSplit.html#sklearn.model_selection.TimeSeriesSplit" title="sklearn.model_selection.TimeSeriesSplit"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.TimeSeriesSplit</span></code></a> to handle time series data.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6586">#6586</a> by <a class="reference external" href="https://github.com/yenchenlin">YenChen Lin</a></p></li>
<li><p>The cross-validation iterators are replaced by cross-validation splitters
available from <a class="reference internal" href="../api/sklearn.model_selection.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a>, allowing for nested
cross-validation. See <a class="reference internal" href="#model-selection-changes"><span class="std std-ref">Model Selection Enhancements and API Changes</span></a> for more information.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/4294">#4294</a> by <a class="reference external" href="https://github.com/raghavrv">Raghav RV</a>.</p></li>
</ul>
</section>
<section id="id4">
<h4>Enhancements<a class="headerlink" href="#id4" title="Link to this heading">#</a></h4>
<p>Trees and ensembles</p>
<ul class="simple">
<li><p>Added a new splitting criterion for <a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">tree.DecisionTreeRegressor</span></code></a>,
the mean absolute error. This criterion can also be used in
<a class="reference internal" href="../modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.ExtraTreesRegressor</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.RandomForestRegressor</span></code></a>, and the gradient boosting
estimators. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6667">#6667</a> by <a class="reference external" href="https://github.com/nelson-liu">Nelson Liu</a>.</p></li>
<li><p>Added weighted impurity-based early stopping criterion for decision tree
growth. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6954">#6954</a> by <a class="reference external" href="https://github.com/nelson-liu">Nelson Liu</a></p></li>
<li><p>The random forest, extra tree and decision tree estimators now has a
method <code class="docutils literal notranslate"><span class="pre">decision_path</span></code> which returns the decision path of samples in
the tree. By <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</p></li>
<li><p>A new example has been added unveiling the decision tree structure.
By <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</p></li>
<li><p>Random forest, extra trees, decision trees and gradient boosting estimator
accept the parameter <code class="docutils literal notranslate"><span class="pre">min_samples_split</span></code> and <code class="docutils literal notranslate"><span class="pre">min_samples_leaf</span></code>
provided as a percentage of the training samples. By <a class="reference external" href="https://github.com/yelite">yelite</a> and <a class="reference external" href="http://www.ajoly.org">Arnaud Joly</a>.</p></li>
<li><p>Gradient boosting estimators accept the parameter <code class="docutils literal notranslate"><span class="pre">criterion</span></code> to specify
to splitting criterion used in built decision trees.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/6667">#6667</a> by <a class="reference external" href="https://github.com/nelson-liu">Nelson Liu</a>.</p></li>
<li><p>The memory footprint is reduced (sometimes greatly) for
<code class="docutils literal notranslate"><span class="pre">ensemble.bagging.BaseBagging</span></code> and classes that inherit from it,
i.e, <a class="reference internal" href="../modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.BaggingClassifier</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.BaggingRegressor</span></code></a>, and <a class="reference internal" href="../modules/generated/sklearn.ensemble.IsolationForest.html#sklearn.ensemble.IsolationForest" title="sklearn.ensemble.IsolationForest"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.IsolationForest</span></code></a>,
by dynamically generating attribute <code class="docutils literal notranslate"><span class="pre">estimators_samples_</span></code> only when it is
needed. By <a class="reference external" href="https://github.com/staubda">David Staub</a>.</p></li>
<li><p>Added <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> and <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameters for
<a class="reference internal" href="../modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.VotingClassifier</span></code></a> to fit underlying estimators in parallel.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/5805">#5805</a> by <a class="reference external" href="https://github.com/olologin">Ibraim Ganiev</a>.</p></li>
</ul>
<p>Linear, kernelized and related models</p>
<ul class="simple">
<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">linear_model.LogisticRegression</span></code></a>, the SAG solver is now
available in the multinomial case. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/5251">#5251</a> by <a class="reference external" href="https://github.com/TomDLT">Tom Dupre la Tour</a>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.RANSACRegressor</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">svm.LinearSVC</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.svm.LinearSVR.html#sklearn.svm.LinearSVR" title="sklearn.svm.LinearSVR"><code class="xref py py-class docutils literal notranslate"><span class="pre">svm.LinearSVR</span></code></a> now support <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>.
By <a class="reference external" href="https://github.com/Imaculate">Imaculate</a>.</p></li>
<li><p>Add parameter <code class="docutils literal notranslate"><span class="pre">loss</span></code> to <a class="reference internal" href="../modules/generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.RANSACRegressor</span></code></a> to measure the
error on the samples for every trial. By <a class="reference external" href="https://manojbits.wordpress.com">Manoj Kumar</a>.</p></li>
<li><p>Prediction of out-of-sample events with Isotonic Regression
(<a class="reference internal" href="../modules/generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">isotonic.IsotonicRegression</span></code></a>) is now much faster (over 1000x in tests with synthetic
data). By <a class="reference external" href="https://github.com/jarfa">Jonathan Arfa</a>.</p></li>
<li><p>Isotonic regression (<a class="reference internal" href="../modules/generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression" title="sklearn.isotonic.IsotonicRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">isotonic.IsotonicRegression</span></code></a>) now uses a better algorithm to avoid
<code class="docutils literal notranslate"><span class="pre">O(n^2)</span></code> behavior in pathological cases, and is also generally faster
(<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/#6691">##6691</a>). By <a class="reference external" href="https://www.ocf.berkeley.edu/~antonyl/">Antony Lee</a>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB" title="sklearn.naive_bayes.GaussianNB"><code class="xref py py-class docutils literal notranslate"><span class="pre">naive_bayes.GaussianNB</span></code></a> now accepts data-independent class-priors
through the parameter <code class="docutils literal notranslate"><span class="pre">priors</span></code>. By <a class="reference external" href="https://github.com/glemaitre">Guillaume Lemaitre</a>.</p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.ElasticNet</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.Lasso</span></code></a>