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<section id="ridge">
<h1>Ridge<a class="headerlink" href="#ridge" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.linear_model.Ridge">
<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">Ridge</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">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">None</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">solver</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">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">random_state</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/4ee3afa55/sklearn/linear_model/_ridge.py#L1013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge" title="Link to this definition">#</a></dt>
<dd><p>Linear least squares with l2 regularization.</p>
<p>Minimizes the objective function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="o">||</span><span class="n">w</span><span class="o">||^</span><span class="mi">2_2</span>
</pre></div>
</div>
<p>This model solves a regression model where the loss function is
the linear least squares function and regularization is given by
the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape (n_samples, n_targets)).</p>
<p>Read more in the <a class="reference internal" href="../linear_model.html#ridge-regression"><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>
<dt><strong>alpha</strong><span class="classifier">{float, ndarray of shape (n_targets,)}, default=1.0</span></dt><dd><p>Constant that multiplies the L2 term, controlling regularization
strength. <code class="docutils literal notranslate"><span class="pre">alpha</span></code> must be a non-negative float i.e. in <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">inf)</span></code>.</p>
<p>When <code class="docutils literal notranslate"><span class="pre">alpha</span> <span class="pre">=</span> <span class="pre">0</span></code>, the objective is equivalent to ordinary least
squares, solved by the <a class="reference internal" href="sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearRegression</span></code></a> object. For numerical
reasons, using <code class="docutils literal notranslate"><span class="pre">alpha</span> <span class="pre">=</span> <span class="pre">0</span></code> with the <code class="docutils literal notranslate"><span class="pre">Ridge</span></code> object is not advised.
Instead, you should use the <a class="reference internal" href="sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearRegression</span></code></a> object.</p>
<p>If an array is passed, penalties are assumed to be specific to the
targets. Hence they must correspond in number.</p>
</dd>
<dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to fit the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are 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>max_iter</strong><span class="classifier">int, default=None</span></dt><dd><p>Maximum number of iterations for conjugate gradient solver.
For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined
by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000.
For ‘lbfgs’ solver, the default value is 15000.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-4</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 each solver:</p>
<ul class="simple">
<li><p>‘svd’: <code class="docutils literal notranslate"><span class="pre">tol</span></code> has no impact.</p></li>
<li><p>‘cholesky’: <code class="docutils literal notranslate"><span class="pre">tol</span></code> has no impact.</p></li>
<li><p>‘sparse_cg’: norm of residuals smaller than <code class="docutils literal notranslate"><span class="pre">tol</span></code>.</p></li>
<li><p>‘lsqr’: <code class="docutils literal notranslate"><span class="pre">tol</span></code> is set as atol and btol of scipy.sparse.linalg.lsqr,
which control the norm of the residual vector in terms of the norms of
matrix and coefficients.</p></li>
<li><p>‘sag’ and ‘saga’: relative change of coef smaller than <code class="docutils literal notranslate"><span class="pre">tol</span></code>.</p></li>
<li><p>‘lbfgs’: maximum of the absolute (projected) gradient=max|residuals|
smaller than <code class="docutils literal notranslate"><span class="pre">tol</span></code>.</p></li>
</ul>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 1.2: </span>Default value changed from 1e-3 to 1e-4 for consistency with other linear
models.</p>
</div>
</dd>
<dt><strong>solver</strong><span class="classifier">{‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’, ‘lbfgs’}, default=’auto’</span></dt><dd><p>Solver to use in the computational routines:</p>
<ul class="simple">
<li><p>‘auto’ chooses the solver automatically based on the type of data.</p></li>
<li><p>‘svd’ uses a Singular Value Decomposition of X to compute the Ridge
coefficients. It is the most stable solver, in particular more stable
for singular matrices than ‘cholesky’ at the cost of being slower.</p></li>
<li><p>‘cholesky’ uses the standard scipy.linalg.solve function to
obtain a closed-form solution.</p></li>
<li><p>‘sparse_cg’ uses the conjugate gradient solver as found in
scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
more appropriate than ‘cholesky’ for large-scale data
(possibility to set <code class="docutils literal notranslate"><span class="pre">tol</span></code> and <code class="docutils literal notranslate"><span class="pre">max_iter</span></code>).</p></li>
<li><p>‘lsqr’ uses the dedicated regularized least-squares routine
scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative
procedure.</p></li>
<li><p>‘sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses
its improved, unbiased version named SAGA. Both methods also use an
iterative procedure, and are often faster than other solvers when
both n_samples and n_features are large. Note that ‘sag’ and
‘saga’ fast convergence is only guaranteed on features with
approximately the same scale. You can preprocess the data with a
scaler from sklearn.preprocessing.</p></li>
<li><p>‘lbfgs’ uses L-BFGS-B algorithm implemented in
<code class="docutils literal notranslate"><span class="pre">scipy.optimize.minimize</span></code>. It can be used only when <code class="docutils literal notranslate"><span class="pre">positive</span></code>
is True.</p></li>
</ul>
<p>All solvers except ‘svd’ support both dense and sparse data. However, only
‘lsqr’, ‘sag’, ‘sparse_cg’, and ‘lbfgs’ support sparse input when
<code class="docutils literal notranslate"><span class="pre">fit_intercept</span></code> is True.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.17: </span>Stochastic Average Gradient descent solver.</p>
</div>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.19: </span>SAGA solver.</p>
</div>
</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.
Only ‘lbfgs’ solver is supported in this case.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, default=None</span></dt><dd><p>Used when <code class="docutils literal notranslate"><span class="pre">solver</span></code> == ‘sag’ or ‘saga’ to shuffle the data.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a> for details.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.17: </span><code class="docutils literal notranslate"><span class="pre">random_state</span></code> to support Stochastic Average Gradient.</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">ndarray of shape (n_features,) or (n_targets, n_features)</span></dt><dd><p>Weight vector(s).</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">float or ndarray of shape (n_targets,)</span></dt><dd><p>Independent term in decision function. 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_iter_</strong><span class="classifier">None or ndarray of shape (n_targets,)</span></dt><dd><p>Actual number of iterations for each target. Available only for
sag and lsqr solvers. Other solvers will return None.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.17.</span></p>
</div>
</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>
<dt><strong>solver_</strong><span class="classifier">str</span></dt><dd><p>The solver that was used at fit time by the computational
routines.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.5.</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.RidgeClassifier.html#sklearn.linear_model.RidgeClassifier" title="sklearn.linear_model.RidgeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeClassifier</span></code></a></dt><dd><p>Ridge classifier.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RidgeCV</span></code></a></dt><dd><p>Ridge regression with built-in cross validation.</p>
</dd>
<dt><a class="reference internal" href="sklearn.kernel_ridge.KernelRidge.html#sklearn.kernel_ridge.KernelRidge" title="sklearn.kernel_ridge.KernelRidge"><code class="xref py py-class docutils literal notranslate"><span class="pre">KernelRidge</span></code></a></dt><dd><p>Kernel ridge regression combines ridge regression with the kernel trick.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Regularization improves the conditioning of the problem and
reduces the variance of the estimates. Larger values specify stronger
regularization. Alpha corresponds to <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">(2C)</span></code> in other linear
models such as <a class="reference internal" href="sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a> or
<a class="reference internal" href="sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC" title="sklearn.svm.LinearSVC"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearSVC</span></code></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">Ridge</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</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="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">Ridge()</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.Ridge.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/4ee3afa55/sklearn/linear_model/_ridge.py#L1216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit Ridge regression 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">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,) or (n_samples, n_targets)</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">float or ndarray of shape (n_samples,), default=None</span></dt><dd><p>Individual weights for each sample. If given a float, every sample
will have the same weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.Ridge.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/4ee3afa55/sklearn/utils/_metadata_requests.py#L1497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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.Ridge.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/4ee3afa55/sklearn/base.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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.Ridge.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/4ee3afa55/sklearn/linear_model/_base.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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.Ridge.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/4ee3afa55/sklearn/base.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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.Ridge.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.Ridge" title="sklearn.linear_model._ridge.Ridge"><span class="pre">Ridge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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.Ridge.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/4ee3afa55/sklearn/base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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.Ridge.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.Ridge" title="sklearn.linear_model._ridge.Ridge"><span class="pre">Ridge</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.Ridge.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 shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography (CT)."><img alt="" src="../../_images/sphx_glr_plot_tomography_l1_reconstruction_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py"><span class="std std-ref">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</span></a></p>
<div class="sphx-glr-thumbnail-title">Compressive sensing: tomography reconstruction with L1 prior (Lasso)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators."><img alt="" src="../../_images/sphx_glr_plot_prediction_latency_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Prediction Latency</span></a></p>
<div class="sphx-glr-thumbnail-title">Prediction Latency</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates differences between a kernel ridge regression and a Gaussian process regression."><img alt="" src="../../_images/sphx_glr_plot_compare_gpr_krr_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py"><span class="std std-ref">Comparison of kernel ridge and Gaussian process regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of kernel ridge and Gaussian process regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Fit Ridge and HuberRegressor on a dataset with outliers."><img alt="" src="../../_images/sphx_glr_plot_huber_vs_ridge_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py"><span class="std std-ref">HuberRegressor vs Ridge on dataset with strong outliers</span></a></p>
<div class="sphx-glr-thumbnail-title">HuberRegressor vs Ridge on dataset with strong outliers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The present example compares three l1-based regression models on a synthetic signal obtained from sparse and correlated features that are further corrupted with additive gaussian noise:"><img alt="" src="../../_images/sphx_glr_plot_lasso_and_elasticnet_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py"><span class="std std-ref">L1-based models for Sparse Signals</span></a></p>
<div class="sphx-glr-thumbnail-title">L1-based models for Sparse Signals</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the ordinary least squares (OLS) model called LinearRegression in scikit-learn."><img alt="" src="../../_images/sphx_glr_plot_ols_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py"><span class="std std-ref">Ordinary Least Squares Example</span></a></p>
<div class="sphx-glr-thumbnail-title">Ordinary Least Squares Example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Ridge regression is basically minimizing a penalised version of the least-squared function. The penalising shrinks the value of the regression coefficients. Despite the few data points in each dimension, the slope of the prediction is much more stable and the variance in the line itself is greatly reduced, in comparison to that of the standard linear regression"><img alt="" src="../../_images/sphx_glr_plot_ols_ridge_variance_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py"><span class="std std-ref">Ordinary Least Squares and Ridge Regression Variance</span></a></p>
<div class="sphx-glr-thumbnail-title">Ordinary Least Squares and Ridge Regression Variance</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Shows the effect of collinearity in the coefficients of an estimator."><img alt="" src="../../_images/sphx_glr_plot_ridge_path_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py"><span class="std std-ref">Plot Ridge coefficients as a function of the regularization</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot Ridge coefficients as a function of the regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from [1]_ and compares it with a linear model fitted with the usual least squared error and a non-linear GBRT model fitted with the Poisson loss (and a log-link)."><img alt="" src="../../_images/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a></p>
<div class="sphx-glr-thumbnail-title">Poisson regression and non-normal loss</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i:"><img alt="" src="../../_images/sphx_glr_plot_polynomial_interpolation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py"><span class="std std-ref">Polynomial and Spline interpolation</span></a></p>
<div class="sphx-glr-thumbnail-title">Polynomial and Spline interpolation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A model that overfits learns the training data too well, capturing both the underlying patterns and the noise in the data. However, when applied to unseen data, the learned associations may not hold. We normally detect this when we apply our trained predictions to the test data and see the statistical performance drop significantly compared to the training data."><img alt="" src="../../_images/sphx_glr_plot_ridge_coeffs_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py"><span class="std std-ref">Ridge coefficients as a function of the L2 Regularization</span></a></p>
<div class="sphx-glr-thumbnail-title">Ridge coefficients as a function of the L2 Regularization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In linear models, the target value is modeled as a linear combination of the features (see the linear_model User Guide section for a description of a set of linear models available in scikit-learn). Coefficients in multiple linear models represent the relationship between the given feature, X_i and the target, y, assuming that all the other features remain constant (conditional dependence). This is different from plotting X_i versus y and fitting a linear relationship: in that case all possible values of the other features are taken into account in the estimation (marginal dependence)."><img alt="" src="../../_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a></p>
<div class="sphx-glr-thumbnail-title">Common pitfalls in the interpretation of coefficients of linear models</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The IterativeImputer class is very flexible - it can be used with a variety of estimators to do round-robin regression, treating every variable as an output in turn."><img alt="" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></p>
<div class="sphx-glr-thumbnail-title">Imputing missing values with variants of IterativeImputer</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The TargetEncoder replaces each category of a categorical feature with the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship between the categorical feature and the target. To prevent overfitting, TargetEncoder.fit_transform uses an internal cross fitting scheme to encode the training data to be used by a downstream model. This scheme involves splitting the data into k folds and encoding each fold using the encodings learnt using the other k-1 folds. In this example, we demonstrate the importance of the cross fitting procedure to prevent overfitting."><img alt="" src="../../_images/sphx_glr_plot_target_encoder_cross_val_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_target_encoder_cross_val.html#sphx-glr-auto-examples-preprocessing-plot-target-encoder-cross-val-py"><span class="std std-ref">Target Encoder’s Internal Cross fitting</span></a></p>
<div class="sphx-glr-thumbnail-title">Target Encoder's Internal Cross fitting</div>
</div></div></section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.Ridge"><code class="docutils literal notranslate"><span class="pre">Ridge</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.Ridge.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.Ridge.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.Ridge.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.Ridge.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.Ridge.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
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