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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_friedman1</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.make_friedman1"><code class="docutils literal notranslate"><span class="pre">make_friedman1</span></code></a></li>
</ul>
</li>
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<section id="sklearn-datasets-make-friedman1">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.make_friedman1<a class="headerlink" href="#sklearn-datasets-make-friedman1" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.make_friedman1">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">make_friedman1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</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">n_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</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">noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</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/3f89022fa/sklearn/datasets/_samples_generator.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.make_friedman1" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate the “Friedman #1” regression problem.</p>
<p>This dataset is described in Friedman [1] and Breiman [2].</p>
<p>Inputs <code class="docutils literal notranslate"><span class="pre">X</span></code> are independent features uniformly distributed on the interval
[0, 1]. The output <code class="docutils literal notranslate"><span class="pre">y</span></code> is created according to the formula:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mi">20</span> <span class="o">*</span> <span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</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">noise</span> <span class="o">*</span> <span class="n">N</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span>
</pre></div>
</div>
<p>Out of the <code class="docutils literal notranslate"><span class="pre">n_features</span></code> features, only 5 are actually used to compute
<code class="docutils literal notranslate"><span class="pre">y</span></code>. The remaining features are independent of <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
<p>The number of features has to be >= 5.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/sample_generators.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</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>n_samples</strong><span class="classifier">int, default=100</span></dt><dd><p>The number of samples.</p>
</dd>
<dt><strong>n_features</strong><span class="classifier">int, default=10</span></dt><dd><p>The number of features. Should be at least 5.</p>
</dd>
<dt><strong>noise</strong><span class="classifier">float, default=0.0</span></dt><dd><p>The standard deviation of the gaussian noise applied to the output.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines random number generation for dataset noise. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The output values.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r1ae00479a3bb-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>J. Friedman, “Multivariate adaptive regression splines”, The Annals
of Statistics 19 (1), pages 1-67, 1991.</p>
</div>
<div class="citation" id="r1ae00479a3bb-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>L. Breiman, “Bagging predictors”, Machine Learning 24,
pages 123-140, 1996.</p>
</div>
</div>
</dd></dl>
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