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<li><a class="reference internal" href="#"><code class="docutils literal"><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"><span class="pre">sklearn.cross_decomposition.PLSRegression</span></code></a></li>
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<div class="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"><span class="pre">sklearn.cross_decomposition</span></code></a>.PLSRegression<a class="headerlink" href="#sklearn-cross-decomposition-plsregression" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_decomposition.PLSRegression">
<em class="property">class </em><code class="descclassname">sklearn.cross_decomposition.</code><code class="descname">PLSRegression</code><span class="sig-paren">(</span><em>n_components=2</em>, <em>scale=True</em>, <em>max_iter=500</em>, <em>tol=1e-06</em>, <em>copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cross_decomposition/pls_.py#L459"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression" title="Permalink to this definition">¶</a></dt>
<dd><p>PLS regression</p>
<p>PLSRegression implements the PLS 2 blocks regression known as PLS2 or PLS1
in case of one dimensional response.
This class inherits from _PLS with mode=”A”, deflation_mode=”regression”,
norm_y_weights=False and algorithm=”nipals”.</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>n_components</strong> : int, (default 2)</p>
<blockquote>
<div><p>Number of components to keep.</p>
</div></blockquote>
<p><strong>scale</strong> : boolean, (default True)</p>
<blockquote>
<div><p>whether to scale the data</p>
</div></blockquote>
<p><strong>max_iter</strong> : an integer, (default 500)</p>
<blockquote>
<div><p>the maximum number of iterations of the NIPALS inner loop (used
only if algorithm=”nipals”)</p>
</div></blockquote>
<p><strong>tol</strong> : non-negative real</p>
<blockquote>
<div><p>Tolerance used in the iterative algorithm default 1e-06.</p>
</div></blockquote>
<p><strong>copy</strong> : boolean, default True</p>
<blockquote class="last">
<div><p>Whether the deflation should be done on a copy. Let the default
value to True unless you don’t care about side effect</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="17%" />
<col width="83%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>x_weights_</strong></td>
<td>(array, [p, n_components]) X block weights vectors.</td>
</tr>
<tr class="row-even"><td><strong>y_weights_</strong></td>
<td>(array, [q, n_components]) Y block weights vectors.</td>
</tr>
<tr class="row-odd"><td><strong>x_loadings_</strong></td>
<td>(array, [p, n_components]) X block loadings vectors.</td>
</tr>
<tr class="row-even"><td><strong>y_loadings_</strong></td>
<td>(array, [q, n_components]) Y block loadings vectors.</td>
</tr>
<tr class="row-odd"><td><strong>x_scores_</strong></td>
<td>(array, [n_samples, n_components]) X scores.</td>
</tr>
<tr class="row-even"><td><strong>y_scores_</strong></td>
<td>(array, [n_samples, n_components]) Y scores.</td>
</tr>
<tr class="row-odd"><td><strong>x_rotations_</strong></td>
<td>(array, [p, n_components]) X block to latents rotations.</td>
</tr>
<tr class="row-even"><td><strong>y_rotations_</strong></td>
<td>(array, [q, n_components]) Y block to latents rotations.</td>
</tr>
<tr class="row-odd"><td><strong>coef_</strong></td>
<td>(array, [p, q]) The coefficients of the linear model: <code class="docutils literal"><span class="pre">Y</span> <span class="pre">=</span> <span class="pre">X</span> <span class="pre">coef_</span> <span class="pre">+</span> <span class="pre">Err</span></code></td>
</tr>
<tr class="row-even"><td><strong>n_iter_</strong></td>
<td>(array-like) Number of iterations of the NIPALS inner loop for each component.</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>Matrices:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">T</span><span class="p">:</span> <span class="n">x_scores_</span>
<span class="n">U</span><span class="p">:</span> <span class="n">y_scores_</span>
<span class="n">W</span><span class="p">:</span> <span class="n">x_weights_</span>
<span class="n">C</span><span class="p">:</span> <span class="n">y_weights_</span>
<span class="n">P</span><span class="p">:</span> <span class="n">x_loadings_</span>
<span class="n">Q</span><span class="p">:</span> <span class="n">y_loadings__</span>
</pre></div>
</div>
<p>Are computed such that:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">T</span> <span class="n">P</span><span class="o">.</span><span class="n">T</span> <span class="o">+</span> <span class="n">Err</span> <span class="ow">and</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">U</span> <span class="n">Q</span><span class="o">.</span><span class="n">T</span> <span class="o">+</span> <span class="n">Err</span>
<span class="n">T</span><span class="p">[:,</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">Xk</span> <span class="n">W</span><span class="p">[:,</span> <span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_components</span><span class="p">)</span>
<span class="n">U</span><span class="p">[:,</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">Yk</span> <span class="n">C</span><span class="p">[:,</span> <span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_components</span><span class="p">)</span>
<span class="n">x_rotations_</span> <span class="o">=</span> <span class="n">W</span> <span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">T</span> <span class="n">W</span><span class="p">)</span><span class="o">^</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y_rotations_</span> <span class="o">=</span> <span class="n">C</span> <span class="p">(</span><span class="n">Q</span><span class="o">.</span><span class="n">T</span> <span class="n">C</span><span class="p">)</span><span class="o">^</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p>where Xk and Yk are residual matrices at iteration k.</p>
<p><a class="reference external" href="http://www.eigenvector.com/Docs/Wise_pls_properties.pdf">Slides explaining
PLS</a></p>
<p>For each component k, find weights u, v that optimizes:
<code class="docutils literal"><span class="pre">max</span> <span class="pre">corr(Xk</span> <span class="pre">u,</span> <span class="pre">Yk</span> <span class="pre">v)</span> <span class="pre">*</span> <span class="pre">std(Xk</span> <span class="pre">u)</span> <span class="pre">std(Yk</span> <span class="pre">u)</span></code>, such that <code class="docutils literal"><span class="pre">|u|</span> <span class="pre">=</span> <span class="pre">1</span></code></p>
<p>Note that it maximizes both the correlations between the scores and the
intra-block variances.</p>
<p>The residual matrix of X (Xk+1) block is obtained by the deflation on
the current X score: x_score.</p>
<p>The residual matrix of Y (Yk+1) block is obtained by deflation on the
current X score. This performs the PLS regression known as PLS2. This
mode is prediction oriented.</p>
<p>This implementation provides the same results that 3 PLS packages
provided in the R language (R-project):</p>
<blockquote>
<div><ul class="simple">
<li>“mixOmics” with function pls(X, Y, mode = “regression”)</li>
<li>“plspm ” with function plsreg2(X, Y)</li>
<li>“pls” with function oscorespls.fit(X, Y)</li>
</ul>
</div></blockquote>
<p class="rubric">References</p>
<p>Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with
emphasis on the two-block case. Technical Report 371, Department of
Statistics, University of Washington, Seattle, 2000.</p>
<p>In french but still a reference:
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
Editions Technic.</p>
<p class="rubric">Examples</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cross_decomposition</span> <span class="k">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="gp">... </span>
<span class="go">PLSRegression(copy=True, max_iter=500, n_components=2, scale=True,</span>
<span class="go"> tol=1e-06)</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 border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.fit" title="sklearn.cross_decomposition.PLSRegression.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X, Y)</td>
<td>Fit model to data.</td>
</tr>
<tr class="row-even"><td><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"><span class="pre">fit_transform</span></code></a>(X[, y])</td>
<td>Learn and apply the dimension reduction on the train data.</td>
</tr>
<tr class="row-odd"><td><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"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.predict" title="sklearn.cross_decomposition.PLSRegression.predict"><code class="xref py py-obj docutils literal"><span class="pre">predict</span></code></a>(X[, copy])</td>
<td>Apply the dimension reduction learned on the train data.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.score" title="sklearn.cross_decomposition.PLSRegression.score"><code class="xref py py-obj docutils literal"><span class="pre">score</span></code></a>(X, y[, sample_weight])</td>
<td>Returns the coefficient of determination R^2 of the prediction.</td>
</tr>
<tr class="row-even"><td><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"><span class="pre">set_params</span></code></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cross_decomposition.PLSRegression.transform" title="sklearn.cross_decomposition.PLSRegression.transform"><code class="xref py py-obj docutils literal"><span class="pre">transform</span></code></a>(X[, Y, copy])</td>
<td>Apply the dimension reduction learned on the train data.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>n_components=2</em>, <em>scale=True</em>, <em>max_iter=500</em>, <em>tol=1e-06</em>, <em>copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cross_decomposition/pls_.py#L590"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>Y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cross_decomposition/pls_.py#L235"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</div></blockquote>
<p><strong>Y</strong> : array-like, shape = [n_samples, n_targets]</p>
<blockquote class="last">
<div><p>Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.fit_transform">
<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cross_decomposition/pls_.py#L439"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = [n_samples, n_targets]</p>
<blockquote>
<div><p>Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">x_scores if Y is not given, (x_scores, y_scores) otherwise.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L213"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>deep</strong> : boolean, optional</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em>, <em>copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cross_decomposition/pls_.py#L414"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the dimension reduction learned on the train data.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</div></blockquote>
<p><strong>copy</strong> : boolean, default True</p>
<blockquote class="last">
<div><p>Whether to copy X and Y, or perform in-place normalization.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>This call requires the estimation of a p x q matrix, which may
be an issue in high dimensional space.</p>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.score">
<code class="descname">score</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L343"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
sum of squares ((y_true - y_true.mean()) ** 2).sum().
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 y, disregarding the input features,
would get a R^2 score of 0.0.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = (n_samples, n_features)</p>
<blockquote>
<div><p>Test samples. For some estimators this may be a
precomputed kernel matrix instead, shape = (n_samples,
n_samples_fitted], where n_samples_fitted is the number of
samples used in the fitting for the estimator.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = (n_samples) or (n_samples, n_outputs)</p>
<blockquote>
<div><p>True values for X.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array-like, shape = [n_samples], optional</p>
<blockquote>
<div><p>Sample weights.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>score</strong> : float</p>
<blockquote class="last">
<div><p>R^2 of self.predict(X) wrt. y.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L236"><span class="viewcode-link">[source]</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 pipelines). The latter have parameters of the form
<code class="docutils literal"><span class="pre"><component>__<parameter></span></code> so that it’s possible to update each
component of a nested object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">self</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cross_decomposition.PLSRegression.transform">
<code class="descname">transform</code><span class="sig-paren">(</span><em>X</em>, <em>Y=None</em>, <em>copy=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cross_decomposition/pls_.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_decomposition.PLSRegression.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply the dimension reduction learned on the train data.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples and
n_features is the number of predictors.</p>
</div></blockquote>
<p><strong>Y</strong> : array-like, shape = [n_samples, n_targets]</p>
<blockquote>
<div><p>Target vectors, where n_samples is the number of samples and
n_targets is the number of response variables.</p>
</div></blockquote>
<p><strong>copy</strong> : boolean, default True</p>
<blockquote>
<div><p>Whether to copy X and Y, or perform in-place normalization.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">x_scores if Y is not given, (x_scores, y_scores) otherwise.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cross-decomposition-plsregression">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cross_decomposition.PLSRegression</span></code><a class="headerlink" href="#examples-using-sklearn-cross-decomposition-plsregression" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with mu..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" src="../../_images/sphx_glr_plot_compare_cross_decomposition_thumb.png" />
<p class="caption"><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></p>
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