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<section id="huberregressor">
<h1>HuberRegressor<a class="headerlink" href="#huberregressor" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor">
<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">HuberRegressor</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">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.35</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">100</span></span></em>, <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">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">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">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</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/_huber.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor" title="Link to this definition">#</a></dt>
<dd><p>L2-regularized linear regression model that is robust to outliers.</p>
<p>The Huber Regressor optimizes the squared loss for the samples where
<code class="docutils literal notranslate"><span class="pre">|(y</span> <span class="pre">-</span> <span class="pre">Xw</span> <span class="pre">-</span> <span class="pre">c)</span> <span class="pre">/</span> <span class="pre">sigma|</span> <span class="pre"><</span> <span class="pre">epsilon</span></code> and the absolute loss for the samples
where <code class="docutils literal notranslate"><span class="pre">|(y</span> <span class="pre">-</span> <span class="pre">Xw</span> <span class="pre">-</span> <span class="pre">c)</span> <span class="pre">/</span> <span class="pre">sigma|</span> <span class="pre">></span> <span class="pre">epsilon</span></code>, where the model coefficients
<code class="docutils literal notranslate"><span class="pre">w</span></code>, the intercept <code class="docutils literal notranslate"><span class="pre">c</span></code> and the scale <code class="docutils literal notranslate"><span class="pre">sigma</span></code> are parameters
to be optimized. The parameter sigma makes sure that if y is scaled up
or down by a certain factor, one does not need to rescale epsilon to
achieve the same robustness. Note that this does not take into account
the fact that the different features of X may be of different scales.</p>
<p>The Huber loss function has the advantage of not being heavily influenced
by the outliers while not completely ignoring their effect.</p>
<p>Read more in the <a class="reference internal" href="../linear_model.html#huber-regression"><span class="std std-ref">User Guide</span></a></p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.18.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>epsilon</strong><span class="classifier">float, default=1.35</span></dt><dd><p>The parameter epsilon controls the number of samples that should be
classified as outliers. The smaller the epsilon, the more robust it is
to outliers. Epsilon must be in the range <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">inf)</span></code>.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=100</span></dt><dd><p>Maximum number of iterations that
<code class="docutils literal notranslate"><span class="pre">scipy.optimize.minimize(method="L-BFGS-B")</span></code> should run for.</p>
</dd>
<dt><strong>alpha</strong><span class="classifier">float, default=0.0001</span></dt><dd><p>Strength of the squared L2 regularization. Note that the penalty is
equal to <code class="docutils literal notranslate"><span class="pre">alpha</span> <span class="pre">*</span> <span class="pre">||w||^2</span></code>.
Must be in the range <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">inf)</span></code>.</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, default=False</span></dt><dd><p>This is useful if the stored attributes of a previously used model
has to be reused. If set to False, then the coefficients will
be rewritten for every call to fit.
See <a class="reference internal" href="../../glossary.html#term-warm_start"><span class="xref std std-term">the Glossary</span></a>.</p>
</dd>
<dt><strong>fit_intercept</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether or not to fit the intercept. This can be set to False
if the data is already centered around the origin.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-05</span></dt><dd><p>The iteration will stop when
<code class="docutils literal notranslate"><span class="pre">max{|proj</span> <span class="pre">g_i</span> <span class="pre">|</span> <span class="pre">i</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">...,</span> <span class="pre">n}</span></code> <= <code class="docutils literal notranslate"><span class="pre">tol</span></code>
where pg_i is the i-th component of the projected gradient.</p>
</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, shape (n_features,)</span></dt><dd><p>Features got by optimizing the L2-regularized Huber loss.</p>
</dd>
<dt><strong>intercept_</strong><span class="classifier">float</span></dt><dd><p>Bias.</p>
</dd>
<dt><strong>scale_</strong><span class="classifier">float</span></dt><dd><p>The value by which <code class="docutils literal notranslate"><span class="pre">|y</span> <span class="pre">-</span> <span class="pre">Xw</span> <span class="pre">-</span> <span class="pre">c|</span></code> is scaled down.</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>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations that
<code class="docutils literal notranslate"><span class="pre">scipy.optimize.minimize(method="L-BFGS-B")</span></code> has run for.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.20: </span>In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
<code class="docutils literal notranslate"><span class="pre">max_iter</span></code>. <code class="docutils literal notranslate"><span class="pre">n_iter_</span></code> will now report at most <code class="docutils literal notranslate"><span class="pre">max_iter</span></code>.</p>
</div>
</dd>
<dt><strong>outliers_</strong><span class="classifier">array, shape (n_samples,)</span></dt><dd><p>A boolean mask which is set to True where the samples are identified
as outliers.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RANSACRegressor</span></code></a></dt><dd><p>RANSAC (RANdom SAmple Consensus) algorithm.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.TheilSenRegressor.html#sklearn.linear_model.TheilSenRegressor" title="sklearn.linear_model.TheilSenRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TheilSenRegressor</span></code></a></dt><dd><p>Theil-Sen Estimator robust multivariate regression model.</p>
</dd>
<dt><a class="reference internal" href="sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor" title="sklearn.linear_model.SGDRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SGDRegressor</span></code></a></dt><dd><p>Fitted by minimizing a regularized empirical loss with SGD.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="re4616ef910fb-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics
Concomitant scale estimates, pg 172</p>
</div>
<div class="citation" id="re4616ef910fb-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>Art B. Owen (2006), A robust hybrid of lasso and ridge regression.
<a class="reference external" href="https://statweb.stanford.edu/~owen/reports/hhu.pdf">https://statweb.stanford.edu/~owen/reports/hhu.pdf</a></p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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">HuberRegressor</span><span class="p">,</span> <span class="n">LinearRegression</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">>>> </span><span class="n">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">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">coef</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">coef</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">y</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">huber</span> <span class="o">=</span> <span class="n">HuberRegressor</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">huber</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">-7.284...</span>
<span class="gp">>>> </span><span class="n">huber</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">1</span><span class="p">,])</span>
<span class="go">array([806.7200...])</span>
<span class="gp">>>> </span><span class="n">linear</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="nb">print</span><span class="p">(</span><span class="s2">"True coefficients:"</span><span class="p">,</span> <span class="n">coef</span><span class="p">)</span>
<span class="go">True coefficients: [20.4923... 34.1698...]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="s2">"Huber coefficients:"</span><span class="p">,</span> <span class="n">huber</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
<span class="go">Huber coefficients: [17.7906... 31.0106...]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="s2">"Linear Regression coefficients:"</span><span class="p">,</span> <span class="n">linear</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
<span class="go">Linear Regression coefficients: [-1.9221... 7.0226...]</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.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/_huber.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.linear_model.HuberRegressor.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit the model according to the given training data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training vector, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples,)</span></dt><dd><p>Target vector relative to X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like, shape (n_samples,)</span></dt><dd><p>Weight given to each sample.</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 <code class="docutils literal notranslate"><span class="pre">HuberRegressor</span></code> estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.linear_model.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor" title="sklearn.linear_model._huber.HuberRegressor"><span class="pre">HuberRegressor</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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor.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.HuberRegressor" title="sklearn.linear_model._huber.HuberRegressor"><span class="pre">HuberRegressor</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.HuberRegressor.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="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="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="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></section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.linear_model.HuberRegressor"><code class="docutils literal notranslate"><span class="pre">HuberRegressor</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.HuberRegressor.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
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