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<section id="multitaskelasticnet">
<h1>MultiTaskElasticNet<a class="headerlink" href="#multitaskelasticnet" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.linear_model.</span></span><span class="sig-name descname"><span class="pre">MultiTaskElasticNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</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">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</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_X</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">1000</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">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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">selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cyclic'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3238516c1/sklearn/linear_model/_coordinate_descent.py#L2410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet" title="Link to this definition">#</a></dt>
<dd><p>Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.</p>
<p>The optimization objective for MultiTaskElasticNet is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">Y</span> <span class="o">-</span> <span class="n">XW</span><span class="o">||</span><span class="n">_Fro</span><span class="o">^</span><span class="mi">2</span>
<span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">l1_ratio</span> <span class="o">*</span> <span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_21</span>
<span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">alpha</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">l1_ratio</span><span class="p">)</span> <span class="o">*</span> <span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_Fro</span><span class="o">^</span><span class="mi">2</span>
</pre></div>
</div>
<p>Where:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_21</span> <span class="o">=</span> <span class="n">sum_i</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">sum_j</span> <span class="n">W_ij</span> <span class="o">^</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<p>i.e. the sum of norms of each row.</p>
<p>Read more in the <a class="reference internal" href="../linear_model.html#multi-task-elastic-net"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>alpha</strong><span class="classifier">float, default=1.0</span></dt><dd><p>Constant that multiplies the L1/L2 term. Defaults to 1.0.</p>
</dd>
<dt><strong>l1_ratio</strong><span class="classifier">float, default=0.5</span></dt><dd><p>The ElasticNet mixing parameter, with 0 < l1_ratio <= 1.
For l1_ratio = 1 the penalty is an L1/L2 penalty. For l1_ratio = 0 it
is an L2 penalty.
For <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre"><</span> <span class="pre">l1_ratio</span> <span class="pre"><</span> <span class="pre">1</span></code>, the penalty is a combination of L1/L2 and L2.</p>
</dd>
<dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be centered).</p>
</dd>
<dt><strong>copy_X</strong><span class="classifier">bool, default=True</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, X will be copied; else, it may be overwritten.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=1000</span></dt><dd><p>The maximum number of iterations.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-4</span></dt><dd><p>The tolerance for the optimization: if the updates are
smaller than <code class="docutils literal notranslate"><span class="pre">tol</span></code>, the optimization code checks the
dual gap for optimality and continues until it is smaller
than <code class="docutils literal notranslate"><span class="pre">tol</span></code>.</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, default=False</span></dt><dd><p>When set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See <a class="reference internal" href="../../glossary.html#term-warm_start"><span class="xref std std-term">the Glossary</span></a>.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, default=None</span></dt><dd><p>The seed of the pseudo random number generator that selects a random
feature to update. Used when <code class="docutils literal notranslate"><span class="pre">selection</span></code> == ‘random’.
Pass an int for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>selection</strong><span class="classifier">{‘cyclic’, ‘random’}, default=’cyclic’</span></dt><dd><p>If set to ‘random’, a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to ‘random’) often leads to significantly faster convergence
especially when tol is higher than 1e-4.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>intercept_</strong><span class="classifier">ndarray of shape (n_targets,)</span></dt><dd><p>Independent term in decision function.</p>
</dd>
<dt><strong>coef_</strong><span class="classifier">ndarray of shape (n_targets, n_features)</span></dt><dd><p>Parameter vector (W in the cost function formula). If a 1D y is
passed in at fit (non multi-task usage), <code class="docutils literal notranslate"><span class="pre">coef_</span></code> is then a 1D array.
Note that <code class="docutils literal notranslate"><span class="pre">coef_</span></code> stores the transpose of <code class="docutils literal notranslate"><span class="pre">W</span></code>, <code class="docutils literal notranslate"><span class="pre">W.T</span></code>.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations run by the coordinate descent solver to reach
the specified tolerance.</p>
</dd>
<dt><strong>dual_gap_</strong><span class="classifier">float</span></dt><dd><p>The dual gaps at the end of the optimization.</p>
</dd>
<dt><strong>eps_</strong><span class="classifier">float</span></dt><dd><p>The tolerance scaled scaled by the variance of the target <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet.sparse_coef_" title="sklearn.linear_model.MultiTaskElasticNet.sparse_coef_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse_coef_</span></code></a><span class="classifier">sparse matrix of shape (n_features,) or (n_targets, n_features)</span></dt><dd><p>Sparse representation of the fitted <code class="docutils literal notranslate"><span class="pre">coef_</span></code>.</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>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskElasticNetCV</span></code></a></dt><dd><p>Multi-task L1/L2 ElasticNet with built-in cross-validation.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElasticNet</span></code></a></dt><dd><p>Linear regression with combined L1 and L2 priors as regularizer.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.MultiTaskLasso.html#sklearn.linear_model.MultiTaskLasso" title="sklearn.linear_model.MultiTaskLasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskLasso</span></code></a></dt><dd><p>Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The algorithm used to fit the model is coordinate descent.</p>
<p>To avoid unnecessary memory duplication the X and y arguments of the fit
method should be directly passed as Fortran-contiguous numpy arrays.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">MultiTaskElasticNet</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
<span class="go">MultiTaskElasticNet(alpha=0.1)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
<span class="go">[[0.45663524 0.45612256]</span>
<span class="go"> [0.45663524 0.45612256]]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
<span class="go">[0.0872422 0.0872422]</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.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/3238516c1/sklearn/linear_model/_coordinate_descent.py#L2564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit MultiTaskElasticNet model with coordinate descent.</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">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples, n_targets)</span></dt><dd><p>Target. Will be cast to X’s dtype if necessary.</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>
<p class="rubric">Notes</p>
<p>Coordinate descent is an algorithm that considers each column of
data at a time hence it will automatically convert the X input
as a Fortran-contiguous numpy array if necessary.</p>
<p>To avoid memory re-allocation it is advised to allocate the
initial data in memory directly using that format.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.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/3238516c1/sklearn/utils/_metadata_requests.py#L1497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.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.linear_model.MultiTaskElasticNet.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/3238516c1/sklearn/base.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.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.linear_model.MultiTaskElasticNet.path">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">path</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_alphas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alphas</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">precompute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xy</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_X</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">coef_init</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">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_input</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="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/3238516c1/sklearn/linear_model/_coordinate_descent.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.path" title="Link to this definition">#</a></dt>
<dd><p>Compute elastic net path with coordinate descent.</p>
<p>The elastic net optimization function varies for mono and multi-outputs.</p>
<p>For mono-output tasks it is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="o">||</span><span class="n">y</span> <span class="o">-</span> <span class="n">Xw</span><span class="o">||^</span><span class="mi">2_2</span>
<span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">l1_ratio</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||</span><span class="n">_1</span>
<span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">alpha</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">l1_ratio</span><span class="p">)</span> <span class="o">*</span> <span class="o">||</span><span class="n">w</span><span class="o">||^</span><span class="mi">2_2</span>
</pre></div>
</div>
<p>For multi-output tasks it is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">))</span> <span class="o">*</span> <span class="o">||</span><span class="n">Y</span> <span class="o">-</span> <span class="n">XW</span><span class="o">||</span><span class="n">_Fro</span><span class="o">^</span><span class="mi">2</span>
<span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">l1_ratio</span> <span class="o">*</span> <span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_21</span>
<span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">alpha</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">l1_ratio</span><span class="p">)</span> <span class="o">*</span> <span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_Fro</span><span class="o">^</span><span class="mi">2</span>
</pre></div>
</div>
<p>Where:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">||</span><span class="n">W</span><span class="o">||</span><span class="n">_21</span> <span class="o">=</span> \<span class="n">sum_i</span> \<span class="n">sqrt</span><span class="p">{</span>\<span class="n">sum_j</span> <span class="n">w_</span><span class="p">{</span><span class="n">ij</span><span class="p">}</span><span class="o">^</span><span class="mi">2</span><span class="p">}</span>
</pre></div>
</div>
<p>i.e. the sum of norm of each row.</p>
<p>Read more in the <a class="reference internal" href="../linear_model.html#elastic-net"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training data. Pass directly as Fortran-contiguous data to avoid
unnecessary memory duplication. If <code class="docutils literal notranslate"><span class="pre">y</span></code> is mono-output then <code class="docutils literal notranslate"><span class="pre">X</span></code>
can be sparse.</p>
</dd>
<dt><strong>y</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_targets)</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>l1_ratio</strong><span class="classifier">float, default=0.5</span></dt><dd><p>Number between 0 and 1 passed to elastic net (scaling between
l1 and l2 penalties). <code class="docutils literal notranslate"><span class="pre">l1_ratio=1</span></code> corresponds to the Lasso.</p>
</dd>
<dt><strong>eps</strong><span class="classifier">float, default=1e-3</span></dt><dd><p>Length of the path. <code class="docutils literal notranslate"><span class="pre">eps=1e-3</span></code> means that
<code class="docutils literal notranslate"><span class="pre">alpha_min</span> <span class="pre">/</span> <span class="pre">alpha_max</span> <span class="pre">=</span> <span class="pre">1e-3</span></code>.</p>
</dd>
<dt><strong>n_alphas</strong><span class="classifier">int, default=100</span></dt><dd><p>Number of alphas along the regularization path.</p>
</dd>
<dt><strong>alphas</strong><span class="classifier">array-like, default=None</span></dt><dd><p>List of alphas where to compute the models.
If None alphas are set automatically.</p>
</dd>
<dt><strong>precompute</strong><span class="classifier">‘auto’, bool or array-like of shape (n_features, n_features), default=’auto’</span></dt><dd><p>Whether to use a precomputed Gram matrix to speed up
calculations. If set to <code class="docutils literal notranslate"><span class="pre">'auto'</span></code> let us decide. The Gram
matrix can also be passed as argument.</p>
</dd>
<dt><strong>Xy</strong><span class="classifier">array-like of shape (n_features,) or (n_features, n_targets), default=None</span></dt><dd><p>Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.</p>
</dd>
<dt><strong>copy_X</strong><span class="classifier">bool, default=True</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, X will be copied; else, it may be overwritten.</p>
</dd>
<dt><strong>coef_init</strong><span class="classifier">array-like of shape (n_features, ), default=None</span></dt><dd><p>The initial values of the coefficients.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool or int, default=False</span></dt><dd><p>Amount of verbosity.</p>
</dd>
<dt><strong>return_n_iter</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to return the number of iterations or not.</p>
</dd>
<dt><strong>positive</strong><span class="classifier">bool, default=False</span></dt><dd><p>If set to True, forces coefficients to be positive.
(Only allowed when <code class="docutils literal notranslate"><span class="pre">y.ndim</span> <span class="pre">==</span> <span class="pre">1</span></code>).</p>
</dd>
<dt><strong>check_input</strong><span class="classifier">bool, default=True</span></dt><dd><p>If set to False, the input validation checks are skipped (including the
Gram matrix when provided). It is assumed that they are handled
by the caller.</p>
</dd>
<dt><strong>**params</strong><span class="classifier">kwargs</span></dt><dd><p>Keyword arguments passed to the coordinate descent solver.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>alphas</strong><span class="classifier">ndarray of shape (n_alphas,)</span></dt><dd><p>The alphas along the path where models are computed.</p>
</dd>
<dt><strong>coefs</strong><span class="classifier">ndarray of shape (n_features, n_alphas) or (n_targets, n_features, n_alphas)</span></dt><dd><p>Coefficients along the path.</p>
</dd>
<dt><strong>dual_gaps</strong><span class="classifier">ndarray of shape (n_alphas,)</span></dt><dd><p>The dual gaps at the end of the optimization for each alpha.</p>
</dd>
<dt><strong>n_iters</strong><span class="classifier">list of int</span></dt><dd><p>The number of iterations taken by the coordinate descent optimizer to
reach the specified tolerance for each alpha.
(Is returned when <code class="docutils literal notranslate"><span class="pre">return_n_iter</span></code> is set to True).</p>
</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.linear_model.MultiTaskElasticNet" title="sklearn.linear_model.MultiTaskElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskElasticNet</span></code></a></dt><dd><p>Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiTaskElasticNetCV</span></code></a></dt><dd><p>Multi-task L1/L2 ElasticNet with built-in cross-validation.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet" title="sklearn.linear_model.ElasticNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElasticNet</span></code></a></dt><dd><p>Linear regression with combined L1 and L2 priors as regularizer.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ElasticNetCV</span></code></a></dt><dd><p>Elastic Net model with iterative fitting along a regularization path.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>For an example, see
<a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py"><span class="std std-ref">examples/linear_model/plot_lasso_coordinate_descent_path.py</span></a>.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">enet_path</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">true_coef</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">true_coef</span>
<span class="go">array([ 0. , 0. , 0. , 97.9..., 45.7...])</span>
<span class="gp">>>> </span><span class="n">alphas</span><span class="p">,</span> <span class="n">estimated_coef</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">enet_path</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="n">n_alphas</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">alphas</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(3,)</span>
<span class="gp">>>> </span><span class="n">estimated_coef</span>
<span class="go"> array([[ 0. , 0.78..., 0.56...],</span>
<span class="go"> [ 0. , 1.12..., 0.61...],</span>
<span class="go"> [-0. , -2.12..., -1.12...],</span>
<span class="go"> [ 0. , 23.04..., 88.93...],</span>
<span class="go"> [ 0. , 10.63..., 41.56...]])</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3238516c1/sklearn/linear_model/_base.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict using the linear model.</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 or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>Samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>C</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>Returns predicted values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.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/3238516c1/sklearn/base.py#L805"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.score" title="Link 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> w.r.t. <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.linear_model.MultiTaskElasticNet.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_request</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">check_input</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNet"><span class="pre">MultiTaskElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3238516c1/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_fit_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <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>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<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>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>check_input</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">check_input</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter 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>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.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/3238516c1/sklearn/base.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.set_score_request">
<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.linear_model.MultiTaskElasticNet" title="sklearn.linear_model._coordinate_descent.MultiTaskElasticNet"><span class="pre">MultiTaskElasticNet</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3238516c1/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.set_score_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <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>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<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>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</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>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.linear_model.MultiTaskElasticNet.sparse_coef_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sparse_coef_</span></span><a class="headerlink" href="#sklearn.linear_model.MultiTaskElasticNet.sparse_coef_" title="Link to this definition">#</a></dt>
<dd><p>Sparse representation of the fitted <code class="docutils literal notranslate"><span class="pre">coef_</span></code>.</p>
</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.linear_model.MultiTaskElasticNet"><code class="docutils literal notranslate"><span class="pre">MultiTaskElasticNet</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.linear_model.MultiTaskElasticNet.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.linear_model.MultiTaskElasticNet.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.linear_model.MultiTaskElasticNet.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.linear_model.MultiTaskElasticNet.path"><code class="docutils literal notranslate"><span class="pre">path</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.MultiTaskElasticNet.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.MultiTaskElasticNet.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.MultiTaskElasticNet.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.MultiTaskElasticNet.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.linear_model.MultiTaskElasticNet.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.MultiTaskElasticNet.sparse_coef_"><code class="docutils literal notranslate"><span class="pre">sparse_coef_</span></code></a></li>
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