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<section id="version-1-1">
<span id="release-notes-1-1"></span><h1>Version 1.1<a class="headerlink" href="#version-1-1" title="Link to this heading">#</a></h1>
<p>For a short description of the main highlights of the release, please refer to
<a class="reference internal" href="../auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a>.</p>
<p class="rubric">Legend for changelogs</p>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-success">Major Feature</span></span> something big that you couldn’t do before.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-success">Feature</span></span> something that you couldn’t do before.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Efficiency</span></span> an existing feature now may not require as much computation or
memory.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> a miscellaneous minor improvement.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> something that previously didn’t work as documented – or according
to reasonable expectations – should now work.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span> you will need to change your code to have the same effect in the
future; or a feature will be removed in the future.</p></li>
</ul>
<section id="version-1-1-3">
<span id="changes-1-1-3"></span><h2>Version 1.1.3<a class="headerlink" href="#version-1-1-3" title="Link to this heading">#</a></h2>
<p><strong>October 2022</strong></p>
<p>This bugfix release only includes fixes for compatibility with the latest
SciPy release >= 1.9.2. Notable changes include:</p>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Include <code class="docutils literal notranslate"><span class="pre">msvcp140.dll</span></code> in the scikit-learn wheels since it has been
removed in the latest SciPy wheels.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/24631">#24631</a> by <a class="reference external" href="https://github.com/cmarmo">Chiara Marmo</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> Create wheels for Python 3.11.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/24446">#24446</a> by <a class="reference external" href="https://github.com/cmarmo">Chiara Marmo</a>.</p></li>
</ul>
<p>Other bug fixes will be available in the next 1.2 release, which will be
released in the coming weeks.</p>
<p>Note that support for 32-bit Python on Windows has been dropped in this release. This
is due to the fact that SciPy 1.9.2 also dropped the support for that platform.
Windows users are advised to install the 64-bit version of Python instead.</p>
</section>
<section id="version-1-1-2">
<span id="changes-1-1-2"></span><h2>Version 1.1.2<a class="headerlink" href="#version-1-1-2" title="Link to this heading">#</a></h2>
<p><strong>August 2022</strong></p>
<section id="changed-models">
<h3>Changed models<a class="headerlink" href="#changed-models" title="Link to this heading">#</a></h3>
<p>The following estimators and functions, when fit with the same data and
parameters, may produce different models from the previous version. This often
occurs due to changes in the modelling logic (bug fixes or enhancements), or in
random sampling procedures.</p>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <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> now throws a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> when fit with
<code class="docutils literal notranslate"><span class="pre">perplexity>=n_samples</span></code> to ensure mathematical correctness of the algorithm.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/10805">#10805</a> by <a class="reference external" href="https://github.com/MrMathias">Mathias Andersen</a> and
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23471">#23471</a> by <a class="reference external" href="https://github.com/micky774">Meekail Zain</a>.</p></li>
</ul>
</section>
<section id="changelog">
<h3>Changelog<a class="headerlink" href="#changelog" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> A default HTML representation is shown for meta-estimators with invalid
parameters. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/24015">#24015</a> by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Add support for F-contiguous arrays for estimators and functions whose back-end
have been changed in 1.1.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23990">#23990</a> by <a class="reference external" href="https://github.com/jjerphan">Julien Jerphanion</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Wheels are now available for MacOS 10.9 and greater. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23833">#23833</a> by
<a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
</ul>
<section id="sklearn-base">
<h4><a class="reference internal" href="../api/sklearn.base.html#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code></a><a class="headerlink" href="#sklearn-base" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> The <code class="docutils literal notranslate"><span class="pre">get_params</span></code> method of the <a class="reference internal" href="../modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">base.BaseEstimator</span></code></a> class now supports
estimators with <code class="docutils literal notranslate"><span class="pre">type</span></code>-type params that have the <code class="docutils literal notranslate"><span class="pre">get_params</span></code> method.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/24017">#24017</a> by <a class="reference external" href="https://github.com/hsorsky">Henry Sorsky</a>.</p></li>
</ul>
</section>
<section id="sklearn-cluster">
<h4><a class="reference internal" href="../api/sklearn.cluster.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a><a class="headerlink" href="#sklearn-cluster" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Fixed a bug in <a class="reference internal" href="../modules/generated/sklearn.cluster.Birch.html#sklearn.cluster.Birch" title="sklearn.cluster.Birch"><code class="xref py py-class docutils literal notranslate"><span class="pre">cluster.Birch</span></code></a> that could trigger an error when splitting
a node if there are duplicates in the dataset.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23395">#23395</a> by <a class="reference external" href="https://github.com/jeremiedbb">Jérémie du Boisberranger</a>.</p></li>
</ul>
</section>
<section id="sklearn-feature-selection">
<h4><a class="reference internal" href="../api/sklearn.feature_selection.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a><a class="headerlink" href="#sklearn-feature-selection" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <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> defaults to selection
threshold 1e-5 when the estimator is either <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>
or <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">linear_model.ElasticNetCV</span></code></a> with <code class="docutils literal notranslate"><span class="pre">l1_ratio</span></code> equals 1 or
<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">linear_model.LassoCV</span></code></a>.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23636">#23636</a> by <a class="reference external" href="https://github.com/haochunchang">Hao Chun Chang</a>.</p></li>
</ul>
</section>
<section id="sklearn-impute">
<h4><a class="reference internal" href="../api/sklearn.impute.html#module-sklearn.impute" title="sklearn.impute"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code></a><a class="headerlink" href="#sklearn-impute" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer" title="sklearn.impute.SimpleImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">impute.SimpleImputer</span></code></a> uses the dtype seen in <code class="docutils literal notranslate"><span class="pre">fit</span></code> for
<code class="docutils literal notranslate"><span class="pre">transform</span></code> when the dtype is object. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22063">#22063</a> by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
</ul>
</section>
<section id="sklearn-linear-model">
<h4><a class="reference internal" href="../api/sklearn.linear_model.html#module-sklearn.linear_model" title="sklearn.linear_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.linear_model</span></code></a><a class="headerlink" href="#sklearn-linear-model" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Use dtype-aware tolerances for the validation of gram matrices (passed by users
or precomputed). <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22059">#22059</a> by <a class="reference external" href="https://github.com/MalteKurz">Malte S. Kurz</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Fixed an error 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> with
<code class="docutils literal notranslate"><span class="pre">solver="newton-cg"</span></code>, <code class="docutils literal notranslate"><span class="pre">fit_intercept=True</span></code>, and a single feature. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23608">#23608</a>
by <a class="reference external" href="https://github.com/TomDLT">Tom Dupre la Tour</a>.</p></li>
</ul>
</section>
<section id="sklearn-manifold">
<h4><a class="reference internal" href="../api/sklearn.manifold.html#module-sklearn.manifold" title="sklearn.manifold"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.manifold</span></code></a><a class="headerlink" href="#sklearn-manifold" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <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> now throws a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> when fit with
<code class="docutils literal notranslate"><span class="pre">perplexity>=n_samples</span></code> to ensure mathematical correctness of the algorithm.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/10805">#10805</a> by <a class="reference external" href="https://github.com/MrMathias">Mathias Andersen</a> and
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23471">#23471</a> by <a class="reference external" href="https://github.com/micky774">Meekail Zain</a>.</p></li>
</ul>
</section>
<section id="sklearn-metrics">
<h4><a class="reference internal" href="../api/sklearn.metrics.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a><a class="headerlink" href="#sklearn-metrics" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Fixed error message of <a class="reference internal" href="../modules/generated/sklearn.metrics.coverage_error.html#sklearn.metrics.coverage_error" title="sklearn.metrics.coverage_error"><code class="xref py py-class docutils literal notranslate"><span class="pre">metrics.coverage_error</span></code></a> for 1D array input.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23548">#23548</a> by <a class="reference external" href="https://github.com/haochunchang">Hao Chun Chang</a>.</p></li>
</ul>
</section>
<section id="sklearn-preprocessing">
<h4><a class="reference internal" href="../api/sklearn.preprocessing.html#module-sklearn.preprocessing" title="sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code></a><a class="headerlink" href="#sklearn-preprocessing" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder.inverse_transform" title="sklearn.preprocessing.OrdinalEncoder.inverse_transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">preprocessing.OrdinalEncoder.inverse_transform</span></code></a> correctly handles
use cases where <code class="docutils literal notranslate"><span class="pre">unknown_value</span></code> or <code class="docutils literal notranslate"><span class="pre">encoded_missing_value</span></code> is <code class="docutils literal notranslate"><span class="pre">nan</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/24087">#24087</a>
by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
</ul>
</section>
<section id="sklearn-tree">
<h4><a class="reference internal" href="../api/sklearn.tree.html#module-sklearn.tree" title="sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code></a><a class="headerlink" href="#sklearn-tree" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Fixed invalid memory access bug during fit in
<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> and <a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">tree.DecisionTreeClassifier</span></code></a>.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23273">#23273</a> by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
</ul>
</section>
</section>
</section>
<section id="version-1-1-1">
<span id="changes-1-1-1"></span><h2>Version 1.1.1<a class="headerlink" href="#version-1-1-1" title="Link to this heading">#</a></h2>
<p><strong>May 2022</strong></p>
<section id="id1">
<h3>Changelog<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> The error message is improved when importing
<a class="reference internal" href="../modules/generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.HalvingGridSearchCV</span></code></a>,
<a class="reference internal" href="../modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.HalvingRandomSearchCV</span></code></a>, or
<a class="reference internal" href="../modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">impute.IterativeImputer</span></code></a> without importing the experimental flag.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23194">#23194</a> by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> Added an extension in doc/conf.py to automatically generate
the list of estimators that handle NaN values.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23198">#23198</a> by <a class="reference external" href="https://github.com/lisekleiber">Lise Kleiber</a>, <a class="reference external" href="https://github.com/MaxwellLZH">Zhehao Liu</a>
and <a class="reference external" href="https://github.com/cmarmo">Chiara Marmo</a>.</p></li>
</ul>
<section id="sklearn-datasets">
<h4><a class="reference internal" href="../api/sklearn.datasets.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a><a class="headerlink" href="#sklearn-datasets" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Avoid timeouts in <a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml" title="sklearn.datasets.fetch_openml"><code class="xref py py-func docutils literal notranslate"><span class="pre">datasets.fetch_openml</span></code></a> by not passing a
<code class="docutils literal notranslate"><span class="pre">timeout</span></code> argument, <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23358">#23358</a> by <a class="reference external" href="https://github.com/lesteve">Loïc Estève</a>.</p></li>
</ul>
</section>
<section id="sklearn-decomposition">
<h4><a class="reference internal" href="../api/sklearn.decomposition.html#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a><a class="headerlink" href="#sklearn-decomposition" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Avoid spurious warning in <a class="reference internal" href="../modules/generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA" title="sklearn.decomposition.IncrementalPCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.IncrementalPCA</span></code></a> when
<code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre">==</span> <span class="pre">n_components</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23264">#23264</a> by <a class="reference external" href="https://github.com/lucyleeow">Lucy Liu</a>.</p></li>
</ul>
</section>
<section id="id2">
<h4><a class="reference internal" href="../api/sklearn.feature_selection.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a><a class="headerlink" href="#id2" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> The <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> method of <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>
now conducts validation for <code class="docutils literal notranslate"><span class="pre">max_features</span></code> and <code class="docutils literal notranslate"><span class="pre">feature_names_in</span></code> parameters.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23299">#23299</a> by <a class="reference external" href="https://github.com/lorentzbao">Long Bao</a>.</p></li>
</ul>
</section>
<section id="id3">
<h4><a class="reference internal" href="../api/sklearn.metrics.html#module-sklearn.metrics" title="sklearn.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.metrics</span></code></a><a class="headerlink" href="#id3" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Fixes <a class="reference internal" href="../modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve" title="sklearn.metrics.precision_recall_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.precision_recall_curve</span></code></a> to compute precision-recall at 100%
recall. The Precision-Recall curve now displays the last point corresponding to a
classifier that always predicts the positive class: recall=100% and
precision=class balance.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23214">#23214</a> by <a class="reference external" href="https://github.com/stephanecollot">Stéphane Collot</a> and <a class="reference external" href="https://github.com/mbaak">Max Baak</a>.</p></li>
</ul>
</section>
<section id="id4">
<h4><a class="reference internal" href="../api/sklearn.preprocessing.html#module-sklearn.preprocessing" title="sklearn.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.preprocessing</span></code></a><a class="headerlink" href="#id4" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures" title="sklearn.preprocessing.PolynomialFeatures"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.PolynomialFeatures</span></code></a> with <code class="docutils literal notranslate"><span class="pre">degree</span></code> equal to 0
will raise error when <code class="docutils literal notranslate"><span class="pre">include_bias</span></code> is set to False, and outputs a single
constant array when <code class="docutils literal notranslate"><span class="pre">include_bias</span></code> is set to True.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23370">#23370</a> by <a class="reference external" href="https://github.com/MaxwellLZH">Zhehao Liu</a>.</p></li>
</ul>
</section>
<section id="id5">
<h4><a class="reference internal" href="../api/sklearn.tree.html#module-sklearn.tree" title="sklearn.tree"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.tree</span></code></a><a class="headerlink" href="#id5" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Fixes performance regression with low cardinality features for
<a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">tree.DecisionTreeClassifier</span></code></a>,
<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>,
<a class="reference internal" href="../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.RandomForestClassifier</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>,
<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">ensemble.GradientBoostingClassifier</span></code></a>, and
<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">ensemble.GradientBoostingRegressor</span></code></a>.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23410">#23410</a> by <a class="reference external" href="https://github.com/lesteve">Loïc Estève</a>.</p></li>
</ul>
</section>
<section id="sklearn-utils">
<h4><a class="reference internal" href="../api/sklearn.utils.html#module-sklearn.utils" title="sklearn.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.utils</span></code></a><a class="headerlink" href="#sklearn-utils" title="Link to this heading">#</a></h4>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.utils.class_weight.compute_sample_weight.html#sklearn.utils.class_weight.compute_sample_weight" title="sklearn.utils.class_weight.compute_sample_weight"><code class="xref py py-func docutils literal notranslate"><span class="pre">utils.class_weight.compute_sample_weight</span></code></a> now works with sparse <code class="docutils literal notranslate"><span class="pre">y</span></code>.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23115">#23115</a> by <a class="reference external" href="https://github.com/kernc">kernc</a>.</p></li>
</ul>
</section>
</section>
</section>
<section id="version-1-1-0">
<span id="changes-1-1"></span><h2>Version 1.1.0<a class="headerlink" href="#version-1-1-0" title="Link to this heading">#</a></h2>
<p><strong>May 2022</strong></p>
<section id="minimal-dependencies">
<h3>Minimal dependencies<a class="headerlink" href="#minimal-dependencies" title="Link to this heading">#</a></h3>
<p>Version 1.1.0 of scikit-learn requires python 3.8+, numpy 1.17.3+ and
scipy 1.3.2+. Optional minimal dependency is matplotlib 3.1.2+.</p>
</section>
<section id="id6">
<h3>Changed models<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
<p>The following estimators and functions, when fit with the same data and
parameters, may produce different models from the previous version. This often
occurs due to changes in the modelling logic (bug fixes or enhancements), or in
random sampling procedures.</p>
<ul class="simple">
<li><p><span class="raw-html"><span class="badge text-bg-info">Efficiency</span></span> <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> now defaults to <code class="docutils literal notranslate"><span class="pre">algorithm="lloyd"</span></code>
instead of <code class="docutils literal notranslate"><span class="pre">algorithm="auto"</span></code>, which was equivalent to
<code class="docutils literal notranslate"><span class="pre">algorithm="elkan"</span></code>. Lloyd’s algorithm and Elkan’s algorithm converge to the
same solution, up to numerical rounding errors, but in general Lloyd’s
algorithm uses much less memory, and it is often faster.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Efficiency</span></span> Fitting <a class="reference internal" href="../modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">tree.DecisionTreeClassifier</span></code></a>,
<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>,
<a class="reference internal" href="../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.RandomForestClassifier</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>,
<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">ensemble.GradientBoostingClassifier</span></code></a>, and
<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">ensemble.GradientBoostingRegressor</span></code></a> is on average 15% faster than in
previous versions thanks to a new sort algorithm to find the best split.
Models might be different because of a different handling of splits
with tied criterion values: both the old and the new sorting algorithm
are unstable sorting algorithms. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22868">#22868</a> by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> The eigenvectors initialization for <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">cluster.SpectralClustering</span></code></a>
and <a class="reference internal" href="../modules/generated/sklearn.manifold.SpectralEmbedding.html#sklearn.manifold.SpectralEmbedding" title="sklearn.manifold.SpectralEmbedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">manifold.SpectralEmbedding</span></code></a> now samples from a Gaussian when
using the <code class="docutils literal notranslate"><span class="pre">'amg'</span></code> or <code class="docutils literal notranslate"><span class="pre">'lobpcg'</span></code> solver. This change improves numerical
stability of the solver, but may result in a different model.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression" title="sklearn.feature_selection.f_regression"><code class="xref py py-func docutils literal notranslate"><span class="pre">feature_selection.f_regression</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.r_regression.html#sklearn.feature_selection.r_regression" title="sklearn.feature_selection.r_regression"><code class="xref py py-func docutils literal notranslate"><span class="pre">feature_selection.r_regression</span></code></a> will now returned finite score by
default instead of <code class="docutils literal notranslate"><span class="pre">np.nan</span></code> and <code class="docutils literal notranslate"><span class="pre">np.inf</span></code> for some corner case. You can use
<code class="docutils literal notranslate"><span class="pre">force_finite=False</span></code> if you really want to get non-finite values and keep
the old behavior.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Panda’s DataFrames with all non-string columns such as a MultiIndex no
longer warns when passed into an Estimator. Estimators will continue to
ignore the column names in DataFrames with non-string columns. For
<code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> to be defined, columns must be all strings. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22410">#22410</a> by
<a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.KBinsDiscretizer</span></code></a> changed handling of bin edges
slightly, which might result in a different encoding with the same data.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.calibration.calibration_curve.html#sklearn.calibration.calibration_curve" title="sklearn.calibration.calibration_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">calibration.calibration_curve</span></code></a> changed handling of bin
edges slightly, which might result in a different output curve given the same
data.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <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> now uses
the correct variance-scaling coefficient which may result in different model
behavior.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> <a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel.fit" title="sklearn.feature_selection.SelectFromModel.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">feature_selection.SelectFromModel.fit</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel.partial_fit" title="sklearn.feature_selection.SelectFromModel.partial_fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">feature_selection.SelectFromModel.partial_fit</span></code></a> can now be called with
<code class="docutils literal notranslate"><span class="pre">prefit=True</span></code>. <code class="docutils literal notranslate"><span class="pre">estimators_</span></code> will be a deep copy of <code class="docutils literal notranslate"><span class="pre">estimator</span></code> when
<code class="docutils literal notranslate"><span class="pre">prefit=True</span></code>. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23271">#23271</a> by <a class="reference external" href="https://github.com/glemaitre">Guillaume Lemaitre</a>.</p></li>
</ul>
</section>
<section id="id7">
<h3>Changelog<a class="headerlink" href="#id7" title="Link to this heading">#</a></h3>
<ul>
<li><p><span class="raw-html"><span class="badge text-bg-info">Efficiency</span></span> Low-level routines for reductions on pairwise distances
for dense float64 datasets have been refactored. The following functions
and estimators now benefit from improved performances in terms of hardware
scalability and speed-ups:</p>
<ul class="simple">
<li><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances_argmin.html#sklearn.metrics.pairwise_distances_argmin" title="sklearn.metrics.pairwise_distances_argmin"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise_distances_argmin</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html#sklearn.metrics.pairwise_distances_argmin_min" title="sklearn.metrics.pairwise_distances_argmin_min"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise_distances_argmin_min</span></code></a></p></li>
<li><p><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">sklearn.cluster.AffinityPropagation</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.cluster.Birch.html#sklearn.cluster.Birch" title="sklearn.cluster.Birch"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.cluster.Birch</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.cluster.MeanShift.html#sklearn.cluster.MeanShift" title="sklearn.cluster.MeanShift"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.cluster.MeanShift</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS" title="sklearn.cluster.OPTICS"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.cluster.OPTICS</span></code></a></p></li>
<li><p><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">sklearn.cluster.SpectralClustering</span></code></a></p></li>
<li><p><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">sklearn.feature_selection.mutual_info_regression</span></code></a></p></li>
<li><p><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">sklearn.neighbors.KNeighborsClassifier</span></code></a></p></li>
<li><p><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">sklearn.neighbors.KNeighborsRegressor</span></code></a></p></li>
<li><p><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">sklearn.neighbors.RadiusNeighborsClassifier</span></code></a></p></li>
<li><p><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">sklearn.neighbors.RadiusNeighborsRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor" title="sklearn.neighbors.LocalOutlierFactor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.neighbors.LocalOutlierFactor</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors" title="sklearn.neighbors.NearestNeighbors"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.neighbors.NearestNeighbors</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.manifold.Isomap.html#sklearn.manifold.Isomap" title="sklearn.manifold.Isomap"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.manifold.Isomap</span></code></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></p></li>
<li><p><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">sklearn.manifold.TSNE</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.manifold.trustworthiness.html#sklearn.manifold.trustworthiness" title="sklearn.manifold.trustworthiness"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.manifold.trustworthiness</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.semi_supervised.LabelPropagation.html#sklearn.semi_supervised.LabelPropagation" title="sklearn.semi_supervised.LabelPropagation"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.semi_supervised.LabelPropagation</span></code></a></p></li>
<li><p><a class="reference internal" href="../modules/generated/sklearn.semi_supervised.LabelSpreading.html#sklearn.semi_supervised.LabelSpreading" title="sklearn.semi_supervised.LabelSpreading"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.semi_supervised.LabelSpreading</span></code></a></p></li>
</ul>
<p>For instance <a class="reference internal" href="../modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.kneighbors" title="sklearn.neighbors.NearestNeighbors.kneighbors"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.neighbors.NearestNeighbors.kneighbors</span></code></a> and
<a class="reference internal" href="../modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors" title="sklearn.neighbors.NearestNeighbors.radius_neighbors"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.neighbors.NearestNeighbors.radius_neighbors</span></code></a>
can respectively be up to ×20 and ×5 faster than previously on a laptop.</p>
<p>Moreover, implementations of those two algorithms are now suitable
for machine with many cores, making them usable for datasets consisting
of millions of samples.</p>
<p><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/21987">#21987</a>, <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22064">#22064</a>, <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22065">#22065</a>, <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22288">#22288</a> and <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22320">#22320</a>
by <a class="reference external" href="https://github.com/jjerphan">Julien Jerphanion</a>.</p>
</li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> All scikit-learn models now generate a more informative
error message when some input contains unexpected <code class="docutils literal notranslate"><span class="pre">NaN</span></code> or infinite values.
In particular the message contains the input name (“X”, “y” or
“sample_weight”) and if an unexpected <code class="docutils literal notranslate"><span class="pre">NaN</span></code> value is found in <code class="docutils literal notranslate"><span class="pre">X</span></code>, the error
message suggests potential solutions.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/21219">#21219</a> by <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> All scikit-learn models now generate a more informative
error message when setting invalid hyper-parameters with <code class="docutils literal notranslate"><span class="pre">set_params</span></code>.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/21542">#21542</a> by <a class="reference external" href="https://github.com/ogrisel">Olivier Grisel</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-info">Enhancement</span></span> Removes random unique identifiers in the HTML representation.
With this change, jupyter notebooks are reproducible as long as the cells are
run in the same order. <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23098">#23098</a> by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-danger">Fix</span></span> Estimators with <code class="docutils literal notranslate"><span class="pre">non_deterministic</span></code> tag set to <code class="docutils literal notranslate"><span class="pre">True</span></code> will skip both
<code class="docutils literal notranslate"><span class="pre">check_methods_sample_order_invariance</span></code> and <code class="docutils literal notranslate"><span class="pre">check_methods_subset_invariance</span></code> tests.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/22318">#22318</a> by <a class="reference external" href="https://github.com/MaxwellLZH">Zhehao Liu</a>.</p></li>
<li><p><span class="raw-html"><span class="badge text-bg-warning">API Change</span></span> The option for using the log loss, aka binomial or multinomial deviance, via
the <code class="docutils literal notranslate"><span class="pre">loss</span></code> parameters was made more consistent. The preferred way is by
setting the value to <code class="docutils literal notranslate"><span class="pre">"log_loss"</span></code>. Old option names are still valid and
produce the same models, but are deprecated and will be removed in version
1.3.</p>
<ul class="simple">
<li><p>For <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">ensemble.GradientBoostingClassifier</span></code></a>, the <code class="docutils literal notranslate"><span class="pre">loss</span></code> parameter name
“deviance” is deprecated in favor of the new name “log_loss”, which is now the
default.
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/pull/23036">#23036</a> by <a class="reference external" href="https://github.com/lorentzenchr">Christian Lorentzen</a>.</p></li>
<li><p>For <a class="reference internal" href="../modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">ensemble.HistGradientBoostingClassifier</span></code></a>, the <code class="docutils literal notranslate"><span class="pre">loss</span></code> parameter names
“auto”, “binary_crossentropy” and “categorical_crossentropy” are deprecated in
favor of the new name “log_loss”, which is now the default.