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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code>.PLSRegression</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cross-decomposition-plsregression">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cross_decomposition.PLSRegression</span></code></a></li>
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<section id="sklearn-cross-decomposition-plsregression">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cross_decomposition" title="sklearn.cross_decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cross_decomposition</span></code></a>.PLSRegression<a class="headerlink" href="#sklearn-cross-decomposition-plsregression" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression">
<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">PLSRegression</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">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</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/6cb2c5237/sklearn/cross_decomposition/_pls.py#L523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression" title="Permalink to this definition">¶</a></dt>
<dd><p>PLS regression.</p>
<p>PLSRegression is also known as PLS2 or PLS1, depending on the number of
targets.</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">New 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>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>max_iter</strong><span class="classifier">int, default=500</span></dt><dd><p>The maximum number of iterations of the power method when
<code class="docutils literal notranslate"><span class="pre">algorithm='nipals'</span></code>. Ignored otherwise.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-06</span></dt><dd><p>The tolerance used as convergence criteria in the power method: the
algorithm stops whenever the squared norm of <code class="docutils literal notranslate"><span class="pre">u_i</span> <span class="pre">-</span> <span class="pre">u_{i-1}</span></code> is less
than <code class="docutils literal notranslate"><span class="pre">tol</span></code>, where <code class="docutils literal notranslate"><span class="pre">u</span></code> corresponds to the left singular vector.</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 <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> 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 cross-covariance matrices of each
iteration.</p>
</dd>
<dt><strong>y_weights_</strong><span class="classifier">ndarray of shape (n_targets, n_components)</span></dt><dd><p>The right singular vectors of the cross-covariance matrices of each
iteration.</p>
</dd>
<dt><strong>x_loadings_</strong><span class="classifier">ndarray of shape (n_features, n_components)</span></dt><dd><p>The loadings of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>y_loadings_</strong><span class="classifier">ndarray of shape (n_targets, n_components)</span></dt><dd><p>The loadings of <code class="docutils literal notranslate"><span class="pre">Y</span></code>.</p>
</dd>
<dt><strong>x_scores_</strong><span class="classifier">ndarray of shape (n_samples, n_components)</span></dt><dd><p>The transformed training samples.</p>
</dd>
<dt><strong>y_scores_</strong><span class="classifier">ndarray of shape (n_samples, n_components)</span></dt><dd><p>The transformed training targets.</p>
</dd>
<dt><strong>x_rotations_</strong><span class="classifier">ndarray of shape (n_features, n_components)</span></dt><dd><p>The projection matrix used to transform <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>y_rotations_</strong><span class="classifier">ndarray of shape (n_features, n_components)</span></dt><dd><p>The projection matrix used to transform <code class="docutils literal notranslate"><span class="pre">Y</span></code>.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.coef_" title="sklearn.cross_decomposition.PLSRegression.coef_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coef_</span></code></a><span class="classifier">ndarray of shape (n_features, n_targets)</span></dt><dd><p>The coefficients of the linear model.</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">ndarray of shape (n_targets,)</span></dt><dd><p>The intercepts of the linear model such that <code class="docutils literal notranslate"><span class="pre">Y</span></code> is approximated as
<code class="docutils literal notranslate"><span class="pre">Y</span> <span class="pre">=</span> <span class="pre">X</span> <span class="pre">@</span> <span class="pre">coef_</span> <span class="pre">+</span> <span class="pre">intercept_</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.1.</span></p>
</div>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">list of shape (n_components,)</span></dt><dd><p>Number of iterations of the power method, for each
component.</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">New 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>
</dl>
</div>
<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.cross_decomposition</span> <span class="kn">import</span> <span class="n">PLSRegression</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</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="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="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="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">>>> </span><span class="n">Y</span> <span class="o">=</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="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">],</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="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">>>> </span><span class="n">pls2</span> <span class="o">=</span> <span class="n">PLSRegression</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="gp">>>> </span><span class="n">pls2</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="go">PLSRegression()</span>
<span class="gp">>>> </span><span class="n">Y_pred</span> <span class="o">=</span> <span class="n">pls2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.fit" title="sklearn.cross_decomposition.PLSRegression.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, Y)</p></td>
<td><p>Fit model to data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.fit_transform" title="sklearn.cross_decomposition.PLSRegression.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(X[, y])</p></td>
<td><p>Learn and apply the dimension reduction on the train data.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.get_feature_names_out" title="sklearn.cross_decomposition.PLSRegression.get_feature_names_out"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a>([input_features])</p></td>
<td><p>Get output feature names for transformation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.get_params" title="sklearn.cross_decomposition.PLSRegression.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.inverse_transform" title="sklearn.cross_decomposition.PLSRegression.inverse_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inverse_transform</span></code></a>(X[, Y])</p></td>
<td><p>Transform data back to its original space.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.predict" title="sklearn.cross_decomposition.PLSRegression.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, copy])</p></td>
<td><p>Predict targets of given samples.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.score" title="sklearn.cross_decomposition.PLSRegression.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
<td><p>Return the coefficient of determination of the prediction.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.set_params" title="sklearn.cross_decomposition.PLSRegression.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.transform" title="sklearn.cross_decomposition.PLSRegression.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X[, Y, copy])</p></td>
<td><p>Apply the dimension reduction.</p></td>
</tr>
</tbody>
</table>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.coef_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">coef_</span></span><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.coef_" title="Permalink to this definition">¶</a></dt>
<dd><p>The coefficients of the linear model.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.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/6cb2c5237/sklearn/cross_decomposition/_pls.py#L642"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.fit" title="Permalink 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 vectors, 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 predictors.</p>
</dd>
<dt><strong>Y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_targets)</span></dt><dd><p>Target vectors, 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_targets</span></code> is the number of response variables.</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 model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.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/6cb2c5237/sklearn/cross_decomposition/_pls.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Learn and apply the dimension reduction on the train 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 vectors, 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 predictors.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples, n_targets), default=None</span></dt><dd><p>Target vectors, 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_targets</span></code> is the number of response variables.</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">ndarray of shape (n_samples, n_components)</span></dt><dd><p>Return <code class="docutils literal notranslate"><span class="pre">x_scores</span></code> if <code class="docutils literal notranslate"><span class="pre">Y</span></code> is not given, <code class="docutils literal notranslate"><span class="pre">(x_scores,</span> <span class="pre">y_scores)</span></code> otherwise.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.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/6cb2c5237/sklearn/base.py#L909"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_feature_names_out" title="Permalink 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.cross_decomposition.PLSRegression.fit" title="sklearn.cross_decomposition.PLSRegression.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.cross_decomposition.PLSRegression.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/6cb2c5237/sklearn/base.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.get_params" title="Permalink 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.PLSRegression.inverse_transform">
<span class="sig-name descname"><span class="pre">inverse_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/6cb2c5237/sklearn/cross_decomposition/_pls.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.inverse_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform data back to its original space.</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_components)</span></dt><dd><p>New 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_components</span></code> is the number of pls components.</p>
</dd>
<dt><strong>Y</strong><span class="classifier">array-like of shape (n_samples, n_components)</span></dt><dd><p>New target, 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_components</span></code> is the number of pls components.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> data.</p>
</dd>
<dt><strong>Y_reconstructed</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Return the reconstructed <code class="docutils literal notranslate"><span class="pre">X</span></code> target. Only returned when <code class="docutils literal notranslate"><span class="pre">Y</span></code> is given.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This transformation will only be exact if <code class="docutils literal notranslate"><span class="pre">n_components=n_features</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.predict">
<span class="sig-name descname"><span class="pre">predict</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">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/6cb2c5237/sklearn/cross_decomposition/_pls.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict targets of given samples.</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.</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>, or perform in-place normalization.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_targets)</span></dt><dd><p>Returns predicted values.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This call requires the estimation of a matrix of shape
<code class="docutils literal notranslate"><span class="pre">(n_features,</span> <span class="pre">n_targets)</span></code>, which may be an issue in high dimensional
space.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.score">
<span class="sig-name descname"><span class="pre">score</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>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</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/6cb2c5237/sklearn/base.py#L677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination of the prediction.</p>
<p>The coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> is defined as
<span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span>
is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>.
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always predicts
the expected value of <code class="docutils literal notranslate"><span class="pre">y</span></code>, disregarding the input features, would get
a <span class="math notranslate nohighlight">\(R^2\)</span> score of 0.0.</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>Test samples. For some estimators this may be a precomputed
kernel matrix or a list of generic objects instead with shape
<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
is the number of samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> wrt. <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with default value of <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>.
This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cross_decomposition.PLSRegression.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/6cb2c5237/sklearn/base.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.set_params" title="Permalink 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.cross_decomposition.PLSRegression.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>, <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/6cb2c5237/sklearn/cross_decomposition/_pls.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the dimension 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 transform.</p>
</dd>
<dt><strong>Y</strong><span class="classifier">array-like of shape (n_samples, n_targets), default=None</span></dt><dd><p>Target vectors.</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>, or perform in-place normalization.</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, y_scores</strong><span class="classifier">array-like or tuple of array-like</span></dt><dd><p>Return <code class="docutils literal notranslate"><span class="pre">x_scores</span></code> if <code class="docutils literal notranslate"><span class="pre">Y</span></code> is not given, <code class="docutils literal notranslate"><span class="pre">(x_scores,</span> <span class="pre">y_scores)</span></code> otherwise.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="examples-using-sklearn-cross-decomposition-plsregression">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cross_decomposition.PLSRegression</span></code><a class="headerlink" href="#examples-using-sklearn-cross-decomposition-plsregression" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Simple usage of various cross decomposition algorithms:"><figure class="align-default" id="id1">
<img alt="Compare cross decomposition methods" src="../../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" />
<figcaption>
<p><span class="caption-text"><a class="reference internal" href="../../auto_examples/cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py"><span class="std std-ref">Compare cross decomposition methods</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares `Principal Component Regression <https://en.wikipedia.org/wiki/Principal_..."><figure class="align-default" id="id2">
<img alt="Principal Component Regression vs Partial Least Squares Regression" src="../../_images/sphx_glr_plot_pcr_vs_pls_thumb.png" />
<figcaption>
<p><span class="caption-text"><a class="reference internal" href="../../auto_examples/cross_decomposition/plot_pcr_vs_pls.html#sphx-glr-auto-examples-cross-decomposition-plot-pcr-vs-pls-py"><span class="std std-ref">Principal Component Regression vs Partial Least Squares Regression</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
</div><div class="clearer"></div></section>
</section>
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