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<section id="linearregression">
<h1>LinearRegression<a class="headerlink" href="#linearregression" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression">
<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">LinearRegression</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">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">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">n_jobs</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">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/linear_model/_base.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression" title="Link to this definition">#</a></dt>
<dd><p>Ordinary least squares Linear Regression.</p>
<p>LinearRegression fits a linear model with coefficients w = (w1, …, wp)
to minimize the residual sum of squares between the observed targets in
the dataset, and the targets predicted by the linear approximation.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<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 True, X will be copied; else, it may be overwritten.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-6</span></dt><dd><p>The precision of the solution (<code class="docutils literal notranslate"><span class="pre">coef_</span></code>) is determined by <code class="docutils literal notranslate"><span class="pre">tol</span></code> which
specifies a different convergence criterion for the <code class="docutils literal notranslate"><span class="pre">lsqr</span></code> solver.
<code class="docutils literal notranslate"><span class="pre">tol</span></code> is set as <code class="docutils literal notranslate"><span class="pre">atol</span></code> and <code class="docutils literal notranslate"><span class="pre">btol</span></code> of <code class="docutils literal notranslate"><span class="pre">scipy.sparse.linalg.lsqr</span></code> when
fitting on sparse training data. This parameter has no effect when fitting
on dense data.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.7.</span></p>
</div>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. This will only provide
speedup in case of sufficiently large problems, that is if firstly
<code class="docutils literal notranslate"><span class="pre">n_targets</span> <span class="pre">></span> <span class="pre">1</span></code> and secondly <code class="docutils literal notranslate"><span class="pre">X</span></code> is sparse or if <code class="docutils literal notranslate"><span class="pre">positive</span></code> is set
to <code class="docutils literal notranslate"><span class="pre">True</span></code>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
</dd>
<dt><strong>positive</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>, forces the coefficients to be positive. This
option is only supported for dense arrays.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>coef_</strong><span class="classifier">array of shape (n_features, ) or (n_targets, n_features)</span></dt><dd><p>Estimated coefficients for the linear regression problem.
If multiple targets are passed during the fit (y 2D), this
is a 2D array of shape (n_targets, n_features), while if only
one target is passed, this is a 1D array of length n_features.</p>
</dd>
<dt><strong>rank_</strong><span class="classifier">int</span></dt><dd><p>Rank of matrix <code class="docutils literal notranslate"><span class="pre">X</span></code>. Only available when <code class="docutils literal notranslate"><span class="pre">X</span></code> is dense.</p>
</dd>
<dt><strong>singular_</strong><span class="classifier">array of shape (min(X, y),)</span></dt><dd><p>Singular values of <code class="docutils literal notranslate"><span class="pre">X</span></code>. Only available when <code class="docutils literal notranslate"><span class="pre">X</span></code> is dense.</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">float or array of shape (n_targets,)</span></dt><dd><p>Independent term in the linear model. Set to 0.0 if
<code class="docutils literal notranslate"><span class="pre">fit_intercept</span> <span class="pre">=</span> <span class="pre">False</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.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ridge</span></code></a></dt><dd><p>Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso" title="sklearn.linear_model.Lasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Lasso</span></code></a></dt><dd><p>The Lasso is a linear model that estimates sparse coefficients with l1 regularization.</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>Elastic-Net is a linear regression model trained with both l1 and l2 -norm regularization of the coefficients.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>From the implementation point of view, this is just plain Ordinary
Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares
(scipy.optimize.nnls) wrapped as a predictor object.</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">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="c1"># y = 1 * x_0 + 2 * x_1 + 3</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]))</span> <span class="o">+</span> <span class="mi">3</span>
<span class="gp">>>> </span><span class="n">reg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">reg</span><span class="o">.</span><span class="n">score</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">1.0</span>
<span class="gp">>>> </span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span>
<span class="go">array([1., 2.])</span>
<span class="gp">>>> </span><span class="n">reg</span><span class="o">.</span><span class="n">intercept_</span>
<span class="go">np.float64(3.0...)</span>
<span class="gp">>>> </span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]))</span>
<span class="go">array([16.])</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.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>, <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/b54e4deea/sklearn/linear_model/_base.py#L586"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit linear model.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training data.</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 values. Will be cast to X’s dtype if necessary.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Individual weights for each sample.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.17: </span>parameter <em>sample_weight</em> support to LinearRegression.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Fitted Estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.LinearRegression.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/b54e4deea/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.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.LinearRegression.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/b54e4deea/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.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.LinearRegression.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/b54e4deea/sklearn/linear_model/_base.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.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.LinearRegression.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/b54e4deea/sklearn/base.py#L619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.score" title="Link to this definition">#</a></dt>
<dd><p>Return <a class="reference internal" href="../model_evaluation.html#r2-score"><span class="std std-ref">coefficient of determination</span></a> on test data.</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.LinearRegression.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">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 v3.13)"><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 v3.13)"><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 v3.13)"><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.LinearRegression" title="sklearn.linear_model._base.LinearRegression"><span class="pre">LinearRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.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>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.LinearRegression.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/b54e4deea/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.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.LinearRegression.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 v3.13)"><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 v3.13)"><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 v3.13)"><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.LinearRegression" title="sklearn.linear_model._base.LinearRegression"><span class="pre">LinearRegression</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/utils/_metadata_requests.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.LinearRegression.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>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example compares Principal Component Regression (PCR) and Partial Least Squares Regression (PLS) on a toy dataset. Our goal is to illustrate how PLS can outperform PCR when the target is strongly correlated with some directions in the data that have a low variance."><img alt="" src="../../_images/sphx_glr_plot_pcr_vs_pls_thumb.png" />
<p><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></p>
<div class="sphx-glr-thumbnail-title">Principal Component Regression vs Partial Least Squares Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction. We will use three different regressors to predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression). Then the above 3 regressors will be used for the VotingRegressor."><img alt="" src="../../_images/sphx_glr_plot_voting_regressor_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py"><span class="std std-ref">Plot individual and voting regression predictions</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot individual and voting regression predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Machine Learning models are great for measuring statistical associations. Unfortunately, unless we're willing to make strong assumptions about the data, those models are unable to infer causal effects."><img alt="" src="../../_images/sphx_glr_plot_causal_interpretation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_causal_interpretation.html#sphx-glr-auto-examples-inspection-plot-causal-interpretation-py"><span class="std std-ref">Failure of Machine Learning to infer causal effects</span></a></p>
<div class="sphx-glr-thumbnail-title">Failure of Machine Learning to infer causal effects</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares two different bayesian regressors:"><img alt="" src="../../_images/sphx_glr_plot_ard_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py"><span class="std std-ref">Comparing Linear Bayesian Regressors</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparing Linear Bayesian Regressors</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Shown in the plot is how the logistic regression would, in this synthetic dataset, classify values as either 0 or 1, i.e. class one or two, using the logistic curve."><img alt="" src="../../_images/sphx_glr_plot_logistic_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py"><span class="std std-ref">Logistic function</span></a></p>
<div class="sphx-glr-thumbnail-title">Logistic function</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we fit a linear model with positive constraints on the regression coefficients and compare the estimated coefficients to a classic linear regression."><img alt="" src="../../_images/sphx_glr_plot_nnls_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_nnls.html#sphx-glr-auto-examples-linear-model-plot-nnls-py"><span class="std std-ref">Non-negative least squares</span></a></p>
<div class="sphx-glr-thumbnail-title">Non-negative least squares</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="1. Ordinary Least Squares: We illustrate how to use the ordinary least squares (OLS) model, LinearRegression, on a single feature of the diabetes dataset. We train on a subset of the data, evaluate on a test set, and visualize the predictions."><img alt="" src="../../_images/sphx_glr_plot_ols_ridge_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ols_ridge.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-py"><span class="std std-ref">Ordinary Least Squares and Ridge Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Ordinary Least Squares and Ridge Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how quantile regression can predict non-trivial conditional quantiles."><img alt="" src="../../_images/sphx_glr_plot_quantile_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_quantile_regression.html#sphx-glr-auto-examples-linear-model-plot-quantile-regression-py"><span class="std std-ref">Quantile regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Quantile regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we see how to robustly fit a linear model to faulty data using the ransac_regression algorithm."><img alt="" src="../../_images/sphx_glr_plot_ransac_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a></p>
<div class="sphx-glr-thumbnail-title">Robust linear model estimation using RANSAC</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here a sine function is fit with a polynomial of order 3, for values close to zero."><img alt="" src="../../_images/sphx_glr_plot_robust_fit_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py"><span class="std std-ref">Robust linear estimator fitting</span></a></p>
<div class="sphx-glr-thumbnail-title">Robust linear estimator fitting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Computes a Theil-Sen Regression on a synthetic dataset."><img alt="" src="../../_images/sphx_glr_plot_theilsen_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py"><span class="std std-ref">Theil-Sen Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Theil-Sen Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise)."><img alt="" src="../../_images/sphx_glr_plot_isotonic_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_isotonic_regression.html#sphx-glr-auto-examples-miscellaneous-plot-isotonic-regression-py"><span class="std std-ref">Isotonic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Isotonic Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This document shows how you can use the metadata routing mechanism <metadata_routing> in scikit-learn to route metadata to the estimators, scorers, and CV splitters consuming them."><img alt="" src="../../_images/sphx_glr_plot_metadata_routing_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a></p>
<div class="sphx-glr-thumbnail-title">Metadata Routing</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half."><img alt="" src="../../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_multioutput_face_completion.html#sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Face completion with a multi-output estimators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use cross_val_predict together with PredictionErrorDisplay to visualize prediction errors."><img alt="" src="../../_images/sphx_glr_plot_cv_predict_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py"><span class="std std-ref">Plotting Cross-Validated Predictions</span></a></p>
<div class="sphx-glr-thumbnail-title">Plotting Cross-Validated Predictions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions. The plot shows the function that we want to approximate, which is a part of the cosine function. In addition, the samples from the real function and the approximations of different models are displayed. The models have polynomial features of different degrees. We can see that a linear function (polynomial with degree 1) is not sufficient to fit the training samples. This is called underfitting. A polynomial of degree 4 approximates the true function almost perfectly. However, for higher degrees the model will overfit the training data, i.e. it learns the noise of the training data. We evaluate quantitatively overfitting / underfitting by using cross-validation. We calculate the mean squared error (MSE) on the validation set, the higher, the less likely the model generalizes correctly from the training data."><img alt="" src="../../_images/sphx_glr_plot_underfitting_overfitting_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py"><span class="std std-ref">Underfitting vs. Overfitting</span></a></p>
<div class="sphx-glr-thumbnail-title">Underfitting vs. Overfitting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The example compares prediction result of linear regression (linear model) and decision tree (tree based model) with and without discretization of real-valued features."><img alt="" src="../../_images/sphx_glr_plot_discretization_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_discretization.html#sphx-glr-auto-examples-preprocessing-plot-discretization-py"><span class="std std-ref">Using KBinsDiscretizer to discretize continuous features</span></a></p>
<div class="sphx-glr-thumbnail-title">Using KBinsDiscretizer to discretize continuous features</div>
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