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<section id="module-scpanel.SVMRFECV">
<span id="scpanel-svmrfecv"></span><h1>scpanel.SVMRFECV<a class="headerlink" href="#module-scpanel.SVMRFECV" title="Link to this heading"></a></h1>
<p>Recursive feature elimination for feature ranking</p>
<section id="classes">
<h2>Classes<a class="headerlink" href="#classes" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#scpanel.SVMRFECV.RFE" title="scpanel.SVMRFECV.RFE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFE</span></code></a></p></td>
<td><p>Feature ranking with recursive feature elimination.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#scpanel.SVMRFECV.RFECV" title="scpanel.SVMRFECV.RFECV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFECV</span></code></a></p></td>
<td><p>Recursive feature elimination with cross-validation to select the number of features.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#scpanel.SVMRFECV._rfe_single_fit" title="scpanel.SVMRFECV._rfe_single_fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_rfe_single_fit</span></code></a>(rfe, estimator, X, y, train_idx, ...)</p></td>
<td><p>Return the score for a fit across one fold.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-contents">
<h2>Module Contents<a class="headerlink" href="#module-contents" title="Link to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="scpanel.SVMRFECV._rfe_single_fit">
<span class="sig-prename descclassname"><span class="pre">scpanel.SVMRFECV.</span></span><span class="sig-name descname"><span class="pre">_rfe_single_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rfe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <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">train_idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scorer</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="headerlink" href="#scpanel.SVMRFECV._rfe_single_fit" title="Link to this definition"></a></dt>
<dd><p>Return the score for a fit across one fold.</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">scpanel.SVMRFECV.</span></span><span class="sig-name descname"><span class="pre">RFE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">sklearn.svm._classes.SVC</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">n_features_to_select</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">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.SVMRFECV.RFE" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.feature_selection._base.SelectorMixin</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.base.MetaEstimatorMixin</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code></p>
<p>Feature ranking with recursive feature elimination.
Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), the goal of recursive feature elimination
(RFE) is to select features by recursively considering smaller and smaller
sets of features. First, the estimator is trained on the initial set of
features and the importance of each feature is obtained either through
any specific attribute or callable.
Then, the least important features are pruned from current set of features.
That procedure is recursively repeated on the pruned set until the desired
number of features to select is eventually reached.
Read more in the <span class="xref std std-ref">User Guide</span>.
:param estimator: A supervised learning estimator with a <code class="docutils literal notranslate"><span class="pre">fit</span></code> method that provides</p>
<blockquote>
<div><p>information about feature importance
(e.g. <cite>coef_</cite>, <cite>feature_importances_</cite>).</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>n_features_to_select</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default=None</em>) – <p>The number of features to select. If <cite>None</cite>, half of the features are
selected. If integer, the parameter is the absolute number of features
to select. If float between 0 and 1, it is the fraction of features to
select.
.. versionchanged:: 0.24</p>
<blockquote>
<div><p>Added float values for fractions.</p>
</div></blockquote>
</p></li>
<li><p><strong>step</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default=1</em>) – If greater than or equal to 1, then <code class="docutils literal notranslate"><span class="pre">step</span></code> corresponds to the
(integer) number of features to remove at each iteration.
If within (0.0, 1.0), then <code class="docutils literal notranslate"><span class="pre">step</span></code> corresponds to the percentage
(rounded down) of features to remove at each iteration.</p></li>
<li><p><strong>verbose</strong> (<em>int</em><em>, </em><em>default=0</em>) – Controls verbosity of output.</p></li>
<li><p><strong>importance_getter</strong> (<em>str</em><em> or </em><em>callable</em><em>, </em><em>default='auto'</em>) – If ‘auto’, uses the feature importance either through a <cite>coef_</cite>
or <cite>feature_importances_</cite> attributes of estimator.
Also accepts a string that specifies an attribute name/path
for extracting feature importance (implemented with <cite>attrgetter</cite>).
For example, give <cite>regressor_.coef_</cite> in case of
<code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedTargetRegressor</span></code> or
<cite>named_steps.clf.feature_importances_</cite> in case of
class:<cite>~sklearn.pipeline.Pipeline</cite> with its last step named <cite>clf</cite>.
If <cite>callable</cite>, overrides the default feature importance getter.
The callable is passed with the fitted estimator and it should
return importance for each feature.
.. versionadded:: 0.24</p></li>
</ul>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.classes_">
<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.classes_" title="Link to this definition"></a></dt>
<dd><p>The classes labels. Only available when <cite>estimator</cite> is a classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (n_classes,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.estimator_">
<span class="sig-name descname"><span class="pre">estimator_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.estimator_" title="Link to this definition"></a></dt>
<dd><p>The fitted estimator used to select features.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">Estimator</span></code> instance</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.n_features_">
<span class="sig-name descname"><span class="pre">n_features_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.n_features_" title="Link to this definition"></a></dt>
<dd><p>The number of selected features.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.n_features_in_">
<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.n_features_in_" title="Link to this definition"></a></dt>
<dd><p>Number of features seen during <span class="xref std std-term">fit</span>. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 0.24</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.feature_names_in_">
<span class="sig-name descname"><span class="pre">feature_names_in_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.feature_names_in_" title="Link to this definition"></a></dt>
<dd><p>Names of features seen during <span class="xref std std-term">fit</span>. Defined only when <cite>X</cite>
has feature names that are all strings.
.. versionadded:: 1.0</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (<cite>n_features_in_</cite>,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.ranking_">
<span class="sig-name descname"><span class="pre">ranking_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.ranking_" title="Link to this definition"></a></dt>
<dd><p>The feature ranking, such that <code class="docutils literal notranslate"><span class="pre">ranking_[i]</span></code> corresponds to the
ranking position of the i-th feature. Selected (i.e., estimated
best) features are assigned rank 1.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (n_features,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.support_">
<span class="sig-name descname"><span class="pre">support_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.support_" title="Link to this definition"></a></dt>
<dd><p>The mask of selected features.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (n_features,)</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="#scpanel.SVMRFECV.RFECV" title="scpanel.SVMRFECV.RFECV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFECV</span></code></a></dt><dd><p>Recursive feature elimination with built-in cross-validated selection of the best number of features.</p>
</dd>
<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">SelectFromModel</span></code></dt><dd><p>Feature selection based on thresholds of importance weights.</p>
</dd>
<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">SequentialFeatureSelector</span></code></dt><dd><p>Sequential cross-validation based feature selection. Does not rely on importance weights.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Allows NaN/Inf in the input if the underlying estimator does as well.</p>
<p class="rubric">References</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id1" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection
for cancer classification using support vector machines”,
Mach. Learn., 46(1-3), 389–422, 2002.</p>
</aside>
</aside>
<p class="rubric">Examples</p>
<p>The following example shows how to retrieve the 5 most informative
features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFE
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel=”linear”)
>>> selector = RFE(estimator, n_features_to_select=5, step=1)
>>> selector = selector.fit(X, y)
>>> <a href="#id3"><span class="problematic" id="id4">selector.support_</span></a>
array([ True, True, True, True, True, False, False, False, False,</p>
<blockquote>
<div><p>False])</p>
</div></blockquote>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">selector</span><span class="o">.</span><span class="n">ranking_</span>
<span class="go">array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.estimator">
<span class="sig-name descname"><span class="pre">estimator</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.estimator" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.n_features_to_select">
<span class="sig-name descname"><span class="pre">n_features_to_select</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.n_features_to_select" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.step">
<span class="sig-name descname"><span class="pre">step</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.step" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.importance_getter">
<span class="sig-name descname"><span class="pre">importance_getter</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.importance_getter" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.verbose">
<span class="sig-name descname"><span class="pre">verbose</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.verbose" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE._estimator_type">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_estimator_type</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE._estimator_type" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.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><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</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="#scpanel.SVMRFECV.RFE" title="scpanel.SVMRFECV.RFE"><span class="pre">RFE</span></a></span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.fit" title="Link to this definition"></a></dt>
<dd><p>Fit the RFE model and then the underlying estimator on the selected features.
:param X: The training input samples.
:type X: {array-like, sparse matrix} of shape (n_samples, n_features)
:param y: The target values.
:type y: array-like of shape (n_samples,)
:param **fit_params: Additional parameters passed to the <cite>fit</cite> method of the underlying</p>
<blockquote>
<div><p>estimator.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>self</strong> – Fitted estimator.</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>object</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE._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><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_score</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</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="#scpanel.SVMRFECV.RFE" title="scpanel.SVMRFECV.RFE"><span class="pre">RFE</span></a></span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE._fit" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.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="headerlink" href="#scpanel.SVMRFECV.RFE.predict" title="Link to this definition"></a></dt>
<dd><p>Reduce X to the selected features and then predict using the underlying estimator.
:param X: The input samples.
:type X: array of shape [n_samples, n_features]</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>y</strong> – The predicted target values.</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>array of shape [n_samples]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.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="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.SVMRFECV.RFE.score" title="Link to this definition"></a></dt>
<dd><p>Reduce X to the selected features and return the score of the underlying estimator.
:param X: The input samples.
:type X: array of shape [n_samples, n_features]
:param y: The target values.
:type y: array of shape [n_samples]
:param **fit_params: Parameters to pass to the <cite>score</cite> method of the underlying</p>
<blockquote>
<div><p>estimator.
.. versionadded:: 1.0</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>score</strong> – Score of the underlying base estimator computed with the selected
features returned by <cite>rfe.transform(X)</cite> and <cite>y</cite>.</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE._get_support_mask">
<span class="sig-name descname"><span class="pre">_get_support_mask</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.SVMRFECV.RFE._get_support_mask" title="Link to this definition"></a></dt>
<dd><p>Get the boolean mask indicating which features are selected</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>support</strong> – An element is True iff its corresponding feature is selected for
retention.</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>boolean array of shape [# input features]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.decision_function">
<span class="sig-name descname"><span class="pre">decision_function</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="headerlink" href="#scpanel.SVMRFECV.RFE.decision_function" title="Link to this definition"></a></dt>
<dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.
:param X: The input samples. Internally, it will be converted to</p>
<blockquote>
<div><p><code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> and if a sparse matrix is provided
to a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>score</strong> – The decision function of the input samples. The order of the
classes corresponds to that in the attribute <span class="xref std std-term">classes_</span>.
Regression and binary classification produce an array of shape
[n_samples].</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>array, shape = [n_samples, n_classes] or [n_samples]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</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="headerlink" href="#scpanel.SVMRFECV.RFE.predict_proba" title="Link to this definition"></a></dt>
<dd><p>Predict class probabilities for X.
:param X: The input samples. Internally, it will be converted to</p>
<blockquote>
<div><p><code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> and if a sparse matrix is provided
to a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>p</strong> – The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute <span class="xref std std-term">classes_</span>.</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>array of shape (n_samples, n_classes)</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE.predict_log_proba">
<span class="sig-name descname"><span class="pre">predict_log_proba</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="headerlink" href="#scpanel.SVMRFECV.RFE.predict_log_proba" title="Link to this definition"></a></dt>
<dd><p>Predict class log-probabilities for X.
:param X: The input samples.
:type X: array of shape [n_samples, n_features]</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>p</strong> – The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute <span class="xref std std-term">classes_</span>.</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>array of shape (n_samples, n_classes)</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFE._more_tags">
<span class="sig-name descname"><span class="pre">_more_tags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFE._more_tags" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">scpanel.SVMRFECV.</span></span><span class="sig-name descname"><span class="pre">RFECV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">sklearn.svm._classes.SVC</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">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_features_to_select</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</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">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_getter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#scpanel.SVMRFECV.RFE" title="scpanel.SVMRFECV.RFE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFE</span></code></a></p>
<p>Recursive feature elimination with cross-validation to select the number of features.
See glossary entry for <span class="xref std std-term">cross-validation estimator</span>.
Read more in the <span class="xref std std-ref">User Guide</span>.
:param estimator: A supervised learning estimator with a <code class="docutils literal notranslate"><span class="pre">fit</span></code> method that provides</p>
<blockquote>
<div><p>information about feature importance either through a <code class="docutils literal notranslate"><span class="pre">coef_</span></code>
attribute or through a <code class="docutils literal notranslate"><span class="pre">feature_importances_</span></code> attribute.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>step</strong> (<em>int</em><em> or </em><em>float</em><em>, </em><em>default=1</em>) – If greater than or equal to 1, then <code class="docutils literal notranslate"><span class="pre">step</span></code> corresponds to the
(integer) number of features to remove at each iteration.
If within (0.0, 1.0), then <code class="docutils literal notranslate"><span class="pre">step</span></code> corresponds to the percentage
(rounded down) of features to remove at each iteration.
Note that the last iteration may remove fewer than <code class="docutils literal notranslate"><span class="pre">step</span></code> features in
order to reach <code class="docutils literal notranslate"><span class="pre">min_features_to_select</span></code>.</p></li>
<li><p><strong>min_features_to_select</strong> (<em>int</em><em>, </em><em>default=1</em>) – The minimum number of features to be selected. This number of features
will always be scored, even if the difference between the original
feature count and <code class="docutils literal notranslate"><span class="pre">min_features_to_select</span></code> isn’t divisible by
<code class="docutils literal notranslate"><span class="pre">step</span></code>.
.. versionadded:: 0.20</p></li>
<li><p><strong>cv</strong> (<em>int</em><em>, </em><em>cross-validation generator</em><em> or </em><em>an iterable</em><em>, </em><em>default=None</em>) – <p>Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds.
- <span class="xref std std-term">CV splitter</span>,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if <code class="docutils literal notranslate"><span class="pre">y</span></code> is binary or multiclass,
<code class="xref py py-class docutils literal notranslate"><span class="pre">StratifiedKFold</span></code> is used. If the
estimator is a classifier or if <code class="docutils literal notranslate"><span class="pre">y</span></code> is neither binary nor multiclass,
<code class="xref py py-class docutils literal notranslate"><span class="pre">KFold</span></code> is used.
Refer <span class="xref std std-ref">User Guide</span> for the various
cross-validation strategies that can be used here.
.. versionchanged:: 0.22</p>
<blockquote>
<div><p><code class="docutils literal notranslate"><span class="pre">cv</span></code> default value of None changed from 3-fold to 5-fold.</p>
</div></blockquote>
</p></li>
<li><p><strong>scoring</strong> (<em>str</em><em>, </em><em>callable</em><em> or </em><em>None</em><em>, </em><em>default=None</em>) – A string (see model evaluation documentation) or
a scorer callable object / function with signature
<code class="docutils literal notranslate"><span class="pre">scorer(estimator,</span> <span class="pre">X,</span> <span class="pre">y)</span></code>.</p></li>
<li><p><strong>verbose</strong> (<em>int</em><em>, </em><em>default=0</em>) – Controls verbosity of output.</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>default=None</em>) – Number of cores to run in parallel while fitting across folds.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <span class="xref std std-term">Glossary</span>
for more details.
.. versionadded:: 0.18</p></li>
<li><p><strong>importance_getter</strong> (<em>str</em><em> or </em><em>callable</em><em>, </em><em>default='auto'</em>) – If ‘auto’, uses the feature importance either through a <cite>coef_</cite>
or <cite>feature_importances_</cite> attributes of estimator.
Also accepts a string that specifies an attribute name/path
for extracting feature importance.
For example, give <cite>regressor_.coef_</cite> in case of
<code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedTargetRegressor</span></code> or
<cite>named_steps.clf.feature_importances_</cite> in case of
<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code> with its last step named <cite>clf</cite>.
If <cite>callable</cite>, overrides the default feature importance getter.
The callable is passed with the fitted estimator and it should
return importance for each feature.
.. versionadded:: 0.24</p></li>
</ul>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.classes_">
<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.classes_" title="Link to this definition"></a></dt>
<dd><p>The classes labels. Only available when <cite>estimator</cite> is a classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (n_classes,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.estimator_">
<span class="sig-name descname"><span class="pre">estimator_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.estimator_" title="Link to this definition"></a></dt>
<dd><p>The fitted estimator used to select features.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">Estimator</span></code> instance</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.grid_scores_">
<span class="sig-name descname"><span class="pre">grid_scores_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.grid_scores_" title="Link to this definition"></a></dt>
<dd><p>The cross-validation scores such that
<code class="docutils literal notranslate"><span class="pre">grid_scores_[i]</span></code> corresponds to
the CV score of the i-th subset of features.
.. deprecated:: 1.0</p>
<blockquote>
<div><p>The <cite>grid_scores_</cite> attribute is deprecated in version 1.0 in favor
of <cite>cv_results_</cite> and will be removed in version 1.2.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (n_subsets_of_features,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.cv_results_">
<span class="sig-name descname"><span class="pre">cv_results_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.cv_results_" title="Link to this definition"></a></dt>
<dd><p>A dict with keys:
split(k)_test_score : ndarray of shape (n_features,)</p>
<blockquote>
<div><p>The cross-validation scores across (k)th fold.</p>
</div></blockquote>
<dl class="simple">
<dt>mean_test_score<span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>Mean of scores over the folds.</p>
</dd>
<dt>std_test_score<span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>Standard deviation of scores over the folds.</p>
</dd>
</dl>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>dict of ndarrays</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.n_features_">
<span class="sig-name descname"><span class="pre">n_features_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.n_features_" title="Link to this definition"></a></dt>
<dd><p>The number of selected features with cross-validation.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.n_features_in_">
<span class="sig-name descname"><span class="pre">n_features_in_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.n_features_in_" title="Link to this definition"></a></dt>
<dd><p>Number of features seen during <span class="xref std std-term">fit</span>. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 0.24</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.feature_names_in_">
<span class="sig-name descname"><span class="pre">feature_names_in_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.feature_names_in_" title="Link to this definition"></a></dt>
<dd><p>Names of features seen during <span class="xref std std-term">fit</span>. Defined only when <cite>X</cite>
has feature names that are all strings.
.. versionadded:: 1.0</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (<cite>n_features_in_</cite>,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.ranking_">
<span class="sig-name descname"><span class="pre">ranking_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.ranking_" title="Link to this definition"></a></dt>
<dd><p>The feature ranking, such that <cite>ranking_[i]</cite>
corresponds to the ranking
position of the i-th feature.
Selected (i.e., estimated best)
features are assigned rank 1.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>narray of shape (n_features,)</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.support_">
<span class="sig-name descname"><span class="pre">support_</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.support_" title="Link to this definition"></a></dt>
<dd><p>The mask of selected features.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>ndarray of shape (n_features,)</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="#scpanel.SVMRFECV.RFE" title="scpanel.SVMRFECV.RFE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFE</span></code></a></dt><dd><p>Recursive feature elimination.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The size of <code class="docutils literal notranslate"><span class="pre">grid_scores_</span></code> is equal to
<code class="docutils literal notranslate"><span class="pre">ceil((n_features</span> <span class="pre">-</span> <span class="pre">min_features_to_select)</span> <span class="pre">/</span> <span class="pre">step)</span> <span class="pre">+</span> <span class="pre">1</span></code>,
where step is the number of features removed at each iteration.
Allows NaN/Inf in the input if the underlying estimator does as well.</p>
<p class="rubric">References</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id2" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection
for cancer classification using support vector machines”,
Mach. Learn., 46(1-3), 389–422, 2002.</p>
</aside>
</aside>
<p class="rubric">Examples</p>
<p>The following example shows how to retrieve the a-priori not known 5
informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFECV
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel=”linear”)
>>> selector = RFECV(estimator, step=1, cv=5)
>>> selector = selector.fit(X, y)
>>> <a href="#id5"><span class="problematic" id="id6">selector.support_</span></a>
array([ True, True, True, True, True, False, False, False, False,</p>
<blockquote>
<div><p>False])</p>
</div></blockquote>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">selector</span><span class="o">.</span><span class="n">ranking_</span>
<span class="go">array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])</span>
</pre></div>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.estimator">
<span class="sig-name descname"><span class="pre">estimator</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.estimator" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.step">
<span class="sig-name descname"><span class="pre">step</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.step" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.importance_getter">
<span class="sig-name descname"><span class="pre">importance_getter</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.importance_getter" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.cv">
<span class="sig-name descname"><span class="pre">cv</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.cv" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.scoring">
<span class="sig-name descname"><span class="pre">scoring</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.scoring" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.verbose">
<span class="sig-name descname"><span class="pre">verbose</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.verbose" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.n_jobs">
<span class="sig-name descname"><span class="pre">n_jobs</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.n_jobs" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.min_features_to_select">
<span class="sig-name descname"><span class="pre">min_features_to_select</span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.min_features_to_select" title="Link to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="scpanel.SVMRFECV.RFECV.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><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_idx_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_idx_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight_list</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</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="#scpanel.SVMRFECV.RFECV" title="scpanel.SVMRFECV.RFECV"><span class="pre">RFECV</span></a></span></span><a class="headerlink" href="#scpanel.SVMRFECV.RFECV.fit" title="Link to this definition"></a></dt>
<dd><p>Fit the RFE model and automatically tune the number of selected features.
:param X: Training vector, where <cite>n_samples</cite> is the number of samples and</p>
<blockquote>
<div><p><cite>n_features</cite> is the total number of features.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>y</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>)</em>) – Target values (integers for classification, real numbers for
regression).</p></li>
<li><p><strong>groups</strong> (<em>array-like</em><em> of </em><em>shape</em><em> (</em><em>n_samples</em><em>,</em><em>) or </em><em>None</em><em>, </em><em>default=None</em>) – Group labels for the samples used while splitting the dataset into
train/test set. Only used in conjunction with a “Group” <span class="xref std std-term">cv</span>
instance (e.g., <code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code>).
.. versionadded:: 0.20</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>self</strong> – Fitted estimator.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>object</p>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="id0">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">grid_scores_</span></span><a class="headerlink" href="#id0" title="Link to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</section>
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Datasets
Models
