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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code>.clone</a><ul>
<li><a class="reference internal" href="#sklearn.base.clone"><code class="docutils literal notranslate"><span class="pre">clone</span></code></a></li>
</ul>
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<section id="sklearn-base-clone">
<h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.base</span></code></a>.clone<a class="headerlink" href="#sklearn-base-clone" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.base.clone">
<span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">clone</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">safe</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/702316c27/sklearn/base.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.clone" title="Permalink to this definition">¶</a></dt>
<dd><p>Construct a new unfitted estimator with the same parameters.</p>
<p>Clone does a deep copy of the model in an estimator
without actually copying attached data. It returns a new estimator
with the same parameters that has not been fitted on any data.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 1.3: </span>Delegates to <code class="docutils literal notranslate"><span class="pre">estimator.__sklearn_clone__</span></code> if the method exists.</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>estimator</strong><span class="classifier">{list, tuple, set} of estimator instance or a single estimator instance</span></dt><dd><p>The estimator or group of estimators to be cloned.</p>
</dd>
<dt><strong>safe</strong><span class="classifier">bool, default=True</span></dt><dd><p>If safe is False, clone will fall back to a deep copy on objects
that are not estimators. Ignored if <code class="docutils literal notranslate"><span class="pre">estimator.__sklearn_clone__</span></code>
exists.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>estimator</strong><span class="classifier">object</span></dt><dd><p>The deep copy of the input, an estimator if input is an estimator.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>If the estimator’s <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter is an integer (or if the
estimator doesn’t have a <code class="docutils literal notranslate"><span class="pre">random_state</span></code> parameter), an <em>exact clone</em> is
returned: the clone and the original estimator will give the exact same
results. Otherwise, <em>statistical clone</em> is returned: the clone might
return different results from the original estimator. More details can be
found in <a class="reference internal" href="../../common_pitfalls.html#randomness"><span class="std std-ref">Controlling randomness</span></a>.</p>
</dd></dl>
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