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  <section id="plssvd">
<h1>PLSSVD<a class="headerlink" href="#plssvd" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cross_decomposition.</span></span><span class="sig-name descname"><span class="pre">PLSSVD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</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/86d099ec1/sklearn/cross_decomposition/_pls.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD" title="Link to this definition">#</a></dt>
<dd><p>Partial Least Square SVD.</p>
<p>This transformer simply performs a SVD on the cross-covariance matrix
<code class="docutils literal notranslate"><span class="pre">X'y</span></code>. It is able to project both the training data <code class="docutils literal notranslate"><span class="pre">X</span></code> and the targets
<code class="docutils literal notranslate"><span class="pre">y</span></code>. The training data <code class="docutils literal notranslate"><span class="pre">X</span></code> is projected on the left singular vectors, while
the targets are projected on the right singular vectors.</p>
<p>Read more in the <a class="reference internal" href="../cross_decomposition.html#cross-decomposition"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.8.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>n_components</strong><span class="classifier">int, default=2</span></dt><dd><p>The number of components to keep. Should be in <code class="docutils literal notranslate"><span class="pre">[1,</span>
<span class="pre">min(n_samples,</span> <span class="pre">n_features,</span> <span class="pre">n_targets)]</span></code>.</p>
</dd>
<dt><strong>scale</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to scale <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to copy <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> in fit before applying centering, and
potentially scaling. If <code class="docutils literal notranslate"><span class="pre">False</span></code>, these operations will be done inplace,
modifying both arrays.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>x_weights_</strong><span class="classifier">ndarray of shape (n_features, n_components)</span></dt><dd><p>The left singular vectors of the SVD of the cross-covariance matrix.
Used to project <code class="docutils literal notranslate"><span class="pre">X</span></code> in <a class="reference internal" href="#sklearn.cross_decomposition.PLSSVD.transform" title="sklearn.cross_decomposition.PLSSVD.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
</dd>
<dt><strong>y_weights_</strong><span class="classifier">ndarray of (n_targets, n_components)</span></dt><dd><p>The right singular vectors of the SVD of the cross-covariance matrix.
Used to project <code class="docutils literal notranslate"><span class="pre">X</span></code> in <a class="reference internal" href="#sklearn.cross_decomposition.PLSSVD.transform" title="sklearn.cross_decomposition.PLSSVD.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
</dd>
<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>
</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.cross_decomposition.PLSCanonical.html#sklearn.cross_decomposition.PLSCanonical" title="sklearn.cross_decomposition.PLSCanonical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PLSCanonical</span></code></a></dt><dd><p>Partial Least Squares transformer and regressor.</p>
</dd>
<dt><a class="reference internal" href="sklearn.cross_decomposition.CCA.html#sklearn.cross_decomposition.CCA" title="sklearn.cross_decomposition.CCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CCA</span></code></a></dt><dd><p>Canonical Correlation Analysis.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cross_decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">PLSSVD</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">6.2</span><span class="p">,</span> <span class="mf">5.9</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mf">11.9</span><span class="p">,</span> <span class="mf">12.3</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pls</span> <span class="o">=</span> <span class="n">PLSSVD</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</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">&gt;&gt;&gt; </span><span class="n">X_c</span><span class="p">,</span> <span class="n">y_c</span> <span class="o">=</span> <span class="n">pls</span><span class="o">.</span><span class="n">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">&gt;&gt;&gt; </span><span class="n">X_c</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_c</span><span class="o">.</span><span class="n">shape</span>
<span class="go">((4, 2), (4, 2))</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.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></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/cross_decomposition/_pls.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit model to data.</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>Training samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_targets)</span></dt><dd><p>Targets.</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>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/cross_decomposition/_pls.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.fit_transform" title="Link to this definition">#</a></dt>
<dd><p>Learn and apply the dimensionality reduction.</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>Training samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_targets),                 default=None</span></dt><dd><p>Targets.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">array-like or tuple of array-like</span></dt><dd><p>The transformed data <code class="docutils literal notranslate"><span class="pre">X_transformed</span></code> if <code class="docutils literal notranslate"><span class="pre">y</span> <span class="pre">is</span> <span class="pre">not</span> <span class="pre">None</span></code>,
<code class="docutils literal notranslate"><span class="pre">(X_transformed,</span> <span class="pre">y_transformed)</span></code> otherwise.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSSVD.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/86d099ec1/sklearn/base.py#L936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.get_feature_names_out" title="Link to this definition">#</a></dt>
<dd><p>Get output feature names for transformation.</p>
<p>The feature names out will prefixed by the lowercased class name. For
example, if the transformer outputs 3 features, then the feature names
out are: <code class="docutils literal notranslate"><span class="pre">[&quot;class_name0&quot;,</span> <span class="pre">&quot;class_name1&quot;,</span> <span class="pre">&quot;class_name2&quot;]</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>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 <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</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.cross_decomposition.PLSSVD.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/86d099ec1/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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/86d099ec1/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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/86d099ec1/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.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">&quot;default&quot;</span></code>: Default output format of a transformer</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">&quot;pandas&quot;</span></code>: DataFrame output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">&quot;polars&quot;</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">&quot;polars&quot;</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.cross_decomposition.PLSSVD.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/86d099ec1/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.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">&lt;component&gt;__&lt;parameter&gt;</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.cross_decomposition.PLSSVD.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>, <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/86d099ec1/sklearn/cross_decomposition/_pls.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSSVD.transform" title="Link to this definition">#</a></dt>
<dd><p>Apply the dimensionality reduction.</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>Samples to be transformed.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_targets),                 default=None</span></dt><dd><p>Targets.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>x_scores</strong><span class="classifier">array-like or tuple of array-like</span></dt><dd><p>The transformed data <code class="docutils literal notranslate"><span class="pre">X_transformed</span></code> if <code class="docutils literal notranslate"><span class="pre">y</span> <span class="pre">is</span> <span class="pre">not</span> <span class="pre">None</span></code>,
<code class="docutils literal notranslate"><span class="pre">(X_transformed,</span> <span class="pre">y_transformed)</span></code> otherwise.</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.cross_decomposition.PLSSVD"><code class="docutils literal notranslate"><span class="pre">PLSSVD</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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.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.cross_decomposition.PLSSVD.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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