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<section id="additivechi2sampler">
<h1>AdditiveChi2Sampler<a class="headerlink" href="#additivechi2sampler" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.kernel_approximation.</span></span><span class="sig-name descname"><span class="pre">AdditiveChi2Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/kernel_approximation.py#L571"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler" title="Link to this definition">#</a></dt>
<dd><p>Approximate feature map for additive chi2 kernel.</p>
<p>Uses sampling the fourier transform of the kernel characteristic
at regular intervals.</p>
<p>Since the kernel that is to be approximated is additive, the components of
the input vectors can be treated separately. Each entry in the original
space is transformed into 2*sample_steps-1 features, where sample_steps is
a parameter of the method. Typical values of sample_steps include 1, 2 and
3.</p>
<p>Optimal choices for the sampling interval for certain data ranges can be
computed (see the reference). The default values should be reasonable.</p>
<p>Read more in the <a class="reference internal" href="../kernel_approximation.html#additive-chi-kernel-approx"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_steps</strong><span class="classifier">int, default=2</span></dt><dd><p>Gives the number of (complex) sampling points.</p>
</dd>
<dt><strong>sample_interval</strong><span class="classifier">float, default=None</span></dt><dd><p>Sampling interval. Must be specified when sample_steps not in {1,2,3}.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.kernel_approximation.SkewedChi2Sampler.html#sklearn.kernel_approximation.SkewedChi2Sampler" title="sklearn.kernel_approximation.SkewedChi2Sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SkewedChi2Sampler</span></code></a></dt><dd><p>A Fourier-approximation to a non-additive variant of the chi squared kernel.</p>
</dd>
<dt><a class="reference internal" href="sklearn.metrics.pairwise.chi2_kernel.html#sklearn.metrics.pairwise.chi2_kernel" title="sklearn.metrics.pairwise.chi2_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.chi2_kernel</span></code></a></dt><dd><p>The exact chi squared kernel.</p>
</dd>
<dt><a class="reference internal" href="sklearn.metrics.pairwise.additive_chi2_kernel.html#sklearn.metrics.pairwise.additive_chi2_kernel" title="sklearn.metrics.pairwise.additive_chi2_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.additive_chi2_kernel</span></code></a></dt><dd><p>The exact additive chi squared kernel.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>This estimator approximates a slightly different version of the additive
chi squared kernel then <code class="docutils literal notranslate"><span class="pre">metric.additive_chi2</span></code> computes.</p>
<p>This estimator is stateless and does not need to be fitted. However, we
recommend to call <a class="reference internal" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform" title="sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit_transform</span></code></a> instead of <a class="reference internal" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.transform" title="sklearn.kernel_approximation.AdditiveChi2Sampler.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>, as
parameter validation is only performed in <a class="reference internal" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit" title="sklearn.kernel_approximation.AdditiveChi2Sampler.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>.</p>
<p class="rubric">References</p>
<p>See <a class="reference external" href="http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf">“Efficient additive kernels via explicit feature maps”</a>
A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
2011</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.kernel_approximation</span> <span class="kn">import</span> <span class="n">AdditiveChi2Sampler</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">chi2sampler</span> <span class="o">=</span> <span class="n">AdditiveChi2Sampler</span><span class="p">(</span><span class="n">sample_steps</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_transformed</span> <span class="o">=</span> <span class="n">chi2sampler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">SGDClassifier</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_transformed</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">SGDClassifier(max_iter=5, random_state=0)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_transformed</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">0.9499...</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/kernel_approximation.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit" title="Link to this definition">#</a></dt>
<dd><p>Only validates estimator’s parameters.</p>
<p>This method allows to: (i) validate the estimator’s parameters and
(ii) be consistent with the scikit-learn transformer API.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples
and <code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples,) or (n_samples, n_outputs), default=None</span></dt><dd><p>Target values (None for unsupervised transformations).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the transformer.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform">
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform" title="Link to this definition">#</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs), default=None</span></dt><dd><p>Target values (None for unsupervised transformations).</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray array of shape (n_samples, n_features_new)</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out">
<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/kernel_approximation.py#L737"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out" title="Link to this definition">#</a></dt>
<dd><p>Get output feature names for transformation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>input_features</strong><span class="classifier">array-like of str or None, default=None</span></dt><dd><p>Only used to validate feature names with the names seen in <a class="reference internal" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit" title="sklearn.kernel_approximation.AdditiveChi2Sampler.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Transformed feature names.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.set_output">
<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_set_output.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_output" title="Link to this definition">#</a></dt>
<dd><p>Set output container.</p>
<p>See <a class="reference internal" href="../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
for an example on how to use the API.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>transform</strong><span class="classifier">{“default”, “pandas”, “polars”}, default=None</span></dt><dd><p>Configure output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"default"</span></code>: Default output format of a transformer</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"pandas"</span></code>: DataFrame output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"polars"</span></code>: Polars output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: Transform configuration is unchanged</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4: </span><code class="docutils literal notranslate"><span class="pre">"polars"</span></code> option was added.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.kernel_approximation.AdditiveChi2Sampler.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/kernel_approximation.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.transform" title="Link to this definition">#</a></dt>
<dd><p>Apply approximate feature map to X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix}, shape (n_samples, n_features)</span></dt><dd><p>Training data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples
and <code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">{ndarray, sparse matrix}, shape = (n_samples, n_features * (2*sample_steps - 1))</span></dt><dd><p>Whether the return value is an array or sparse matrix depends on
the type of the input X.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler"><code class="docutils literal notranslate"><span class="pre">AdditiveChi2Sampler</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.fit_transform"><code class="docutils literal notranslate"><span class="pre">fit_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_feature_names_out"><code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.kernel_approximation.AdditiveChi2Sampler.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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