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<section id="check-random-state">
<h1>check_random_state<a class="headerlink" href="#check-random-state" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.utils.check_random_state">
<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">check_random_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/validation.py#L1478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.check_random_state" title="Link to this definition">#</a></dt>
<dd><p>Turn seed into a np.random.RandomState instance.</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>seed</strong><span class="classifier">None, int or instance of RandomState</span></dt><dd><p>If seed is None, return the RandomState singleton used by np.random.
If seed is an int, return a new RandomState instance seeded with seed.
If seed is already a RandomState instance, return it.
Otherwise raise ValueError.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.1)"><code class="docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></dt><dd><p>The random state object based on <code class="docutils literal notranslate"><span class="pre">seed</span></code> parameter.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_random_state</span>
<span class="gp">>>> </span><span class="n">check_random_state</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="go">RandomState(MT19937) at 0x...</span>
</pre></div>
</div>
</dd></dl>
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<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="Evaluate the ability of k-means initializations strategies to make the algorithm convergence robust, as measured by the relative standard deviation of the inertia of the clustering (i.e. the sum of squared distances to the nearest cluster center)."><img alt="" src="../../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py"><span class="std std-ref">Empirical evaluation of the impact of k-means initialization</span></a></p>
<div class="sphx-glr-thumbnail-title">Empirical evaluation of the impact of k-means initialization</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the number of samples is significantly larger than the number of features and is able to finely optimize non-smooth objective functions which is the case with the l1-penalty. Test accuracy reaches > 0.8, while weight vectors remains sparse and therefore more easily interpretable."><img alt="" src="../../_images/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classification using multinomial logistic + L1</span></a></p>
<div class="sphx-glr-thumbnail-title">MNIST classification using multinomial logistic + L1</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An application of the different manifold techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold learning methods. Regarding the dataset, the poles are cut from the sphere, as well as a thin slice down its side. This enables the manifold learning techniques to 'spread it open' whilst projecting it onto two dimensions."><img alt="" src="../../_images/sphx_glr_plot_manifold_sphere_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/manifold/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py"><span class="std std-ref">Manifold Learning methods on a severed sphere</span></a></p>
<div class="sphx-glr-thumbnail-title">Manifold Learning methods on a severed sphere</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half."><img alt="" src="../../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_multioutput_face_completion.html#sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Face completion with a multi-output estimators</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise)."><img alt="" src="../../_images/sphx_glr_plot_isotonic_regression_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_isotonic_regression.html#sphx-glr-auto-examples-miscellaneous-plot-isotonic-regression-py"><span class="std std-ref">Isotonic Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Isotonic Regression</div>
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