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<section id="validate-data">
<h1>validate_data<a class="headerlink" href="#validate-data" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.utils.validation.validate_data">
<span class="sig-prename descclassname"><span class="pre">sklearn.utils.validation.</span></span><span class="sig-name descname"><span class="pre">validate_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_estimator</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">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no_validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no_validation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset</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">validate_separately</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">skip_check_array</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">check_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4f29d4e30/sklearn/utils/validation.py#L2833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.validation.validate_data" title="Link to this definition">#</a></dt>
<dd><p>Validate input data and set or check feature names and counts of the input.</p>
<p>This helper function should be used in an estimator that requires input
validation. This mutates the estimator and sets the <code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code> and
<code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> attributes if <code class="docutils literal notranslate"><span class="pre">reset=True</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.6.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>_estimator</strong><span class="classifier">estimator instance</span></dt><dd><p>The estimator to validate the input for.</p>
</dd>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix, dataframe} of shape (n_samples, n_features), default=’no validation’</span></dt><dd><p>The input samples.
If <code class="docutils literal notranslate"><span class="pre">'no_validation'</span></code>, no validation is performed on <code class="docutils literal notranslate"><span class="pre">X</span></code>. This is
useful for meta-estimator which can delegate input validation to
their underlying estimator(s). In that case <code class="docutils literal notranslate"><span class="pre">y</span></code> must be passed and
the only accepted <code class="docutils literal notranslate"><span class="pre">check_params</span></code> are <code class="docutils literal notranslate"><span class="pre">multi_output</span></code> and
<code class="docutils literal notranslate"><span class="pre">y_numeric</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,), default=’no_validation’</span></dt><dd><p>The targets.</p>
<ul class="simple">
<li><p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, <a class="reference internal" href="sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_array</span></code></a> is called on <code class="docutils literal notranslate"><span class="pre">X</span></code>. If
the estimator’s <code class="docutils literal notranslate"><span class="pre">requires_y</span></code> tag is True, then an error will be raised.</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">'no_validation'</span></code>, <a class="reference internal" href="sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_array</span></code></a> is called
on <code class="docutils literal notranslate"><span class="pre">X</span></code> and the estimator’s <code class="docutils literal notranslate"><span class="pre">requires_y</span></code> tag is ignored. This is a default
placeholder and is never meant to be explicitly set. In that case <code class="docutils literal notranslate"><span class="pre">X</span></code> must be
passed.</p></li>
<li><p>Otherwise, only <code class="docutils literal notranslate"><span class="pre">y</span></code> with <code class="docutils literal notranslate"><span class="pre">_check_y</span></code> or both <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are checked with
either <a class="reference internal" href="sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_array</span></code></a> or
<a class="reference internal" href="sklearn.utils.check_X_y.html#sklearn.utils.check_X_y" title="sklearn.utils.check_X_y"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_X_y</span></code></a> depending on <code class="docutils literal notranslate"><span class="pre">validate_separately</span></code>.</p></li>
</ul>
</dd>
<dt><strong>reset</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to reset the <code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code> attribute.
If False, the input will be checked for consistency with data
provided when reset was last True.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is recommended to call <code class="docutils literal notranslate"><span class="pre">reset=True</span></code> in <code class="docutils literal notranslate"><span class="pre">fit</span></code> and in the first
call to <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code>. All other methods that validate <code class="docutils literal notranslate"><span class="pre">X</span></code>
should set <code class="docutils literal notranslate"><span class="pre">reset=False</span></code>.</p>
</div>
</dd>
<dt><strong>validate_separately</strong><span class="classifier">False or tuple of dicts, default=False</span></dt><dd><p>Only used if <code class="docutils literal notranslate"><span class="pre">y</span></code> is not <code class="docutils literal notranslate"><span class="pre">None</span></code>.
If <code class="docutils literal notranslate"><span class="pre">False</span></code>, call <a class="reference internal" href="sklearn.utils.check_X_y.html#sklearn.utils.check_X_y" title="sklearn.utils.check_X_y"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_X_y</span></code></a>. Else, it must be a tuple of
kwargs to be used for calling <a class="reference internal" href="sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_array</span></code></a> on <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>
respectively.</p>
<p><code class="docutils literal notranslate"><span class="pre">estimator=self</span></code> is automatically added to these dicts to generate
more informative error message in case of invalid input data.</p>
</dd>
<dt><strong>skip_check_array</strong><span class="classifier">bool, default=False</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are unchanged and only <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> and
<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code> are checked. Otherwise, <a class="reference internal" href="sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_array</span></code></a>
is called on <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
<dt><strong>**check_params</strong><span class="classifier">kwargs</span></dt><dd><p>Parameters passed to <a class="reference internal" href="sklearn.utils.check_array.html#sklearn.utils.check_array" title="sklearn.utils.check_array"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_array</span></code></a> or
<a class="reference internal" href="sklearn.utils.check_X_y.html#sklearn.utils.check_X_y" title="sklearn.utils.check_X_y"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_X_y</span></code></a>. Ignored if validate_separately
is not False.</p>
<p><code class="docutils literal notranslate"><span class="pre">estimator=self</span></code> is automatically added to these params to generate
more informative error message in case of invalid input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">{ndarray, sparse matrix} or tuple of these</span></dt><dd><p>The validated input. A tuple is returned if both <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are
validated.</p>
</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="We are pleased to announce the release of scikit-learn 1.6! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes <release_notes_1_6>."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_6_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_6_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-6-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.6</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.6</div>
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