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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_friedman3</a></li>
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<div class="section" id="sklearn-datasets-make-friedman3">
<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_friedman3<a class="headerlink" href="#sklearn-datasets-make-friedman3" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.datasets.make_friedman3">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">make_friedman3</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n_samples</span><span class="o">=</span><span class="default_value">100</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">noise</span><span class="o">=</span><span class="default_value">0.0</span></em>, <em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/datasets/_samples_generator.py#L1039"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_friedman3" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate the “Friedman #3” 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 4 independent features uniformly distributed on the
intervals:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">0</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="mi">100</span><span class="p">,</span>
<span class="mi">40</span> <span class="o">*</span> <span class="n">pi</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">560</span> <span class="o">*</span> <span class="n">pi</span><span class="p">,</span>
<span class="mi">0</span> <span class="o"><=</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="mi">1</span><span class="p">,</span>
<span class="mi">1</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="mf">11.</span>
</pre></div>
</div>
<p>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="n">arctan</span><span class="p">((</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="n">X</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</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="n">X</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]))</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">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>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</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>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</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, 4)</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>
<dl class="citation">
<dt class="label" id="rff0a8e3dd726-1"><span class="brackets">1</span></dt>
<dd><p>J. Friedman, “Multivariate adaptive regression splines”, The Annals
of Statistics 19 (1), pages 1-67, 1991.</p>
</dd>
<dt class="label" id="rff0a8e3dd726-2"><span class="brackets">2</span></dt>
<dd><p>L. Breiman, “Bagging predictors”, Machine Learning 24,
pages 123-140, 1996.</p>
</dd>
</dl>
</dd></dl>
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