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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.make_friedman1</a></li>
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<div class="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"><span class="pre">sklearn.datasets</span></code></a>.make_friedman1<a class="headerlink" href="#sklearn-datasets-make-friedman1" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_friedman1">
<code class="descclassname">sklearn.datasets.</code><code class="descname">make_friedman1</code><span class="sig-paren">(</span><em>n_samples=100</em>, <em>n_features=10</em>, <em>noise=0.0</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/datasets/samples_generator.py#L776"><span class="viewcode-link">[source]</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 <cite>X</cite> are independent features uniformly distributed on the interval
[0, 1]. The output <cite>y</cite> is created according to the formula:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).
</pre></div>
</div>
<p>Out of the <cite>n_features</cite> features, only 5 are actually used to compute
<cite>y</cite>. The remaining features are independent of <cite>y</cite>.</p>
<p>The number of features has to be >= 5.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><span>User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>n_samples</strong> : int, optional (default=100)</p>
<blockquote>
<div><p>The number of samples.</p>
</div></blockquote>
<p><strong>n_features</strong> : int, optional (default=10)</p>
<blockquote>
<div><p>The number of features. Should be at least 5.</p>
</div></blockquote>
<p><strong>noise</strong> : float, optional (default=0.0)</p>
<blockquote>
<div><p>The standard deviation of the gaussian noise applied to the output.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None, optional (default=None)</p>
<blockquote>
<div><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <cite>np.random</cite>.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X</strong> : array of shape [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
<p><strong>y</strong> : array of shape [n_samples]</p>
<blockquote class="last">
<div><p>The output values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<table class="docutils citation" frame="void" id="r111" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[R111]</a></td><td>J. Friedman, “Multivariate adaptive regression splines”, The Annals
of Statistics 19 (1), pages 1-67, 1991.</td></tr>
</tbody>
</table>
<table class="docutils citation" frame="void" id="r112" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id2">[R112]</a></td><td>L. Breiman, “Bagging predictors”, Machine Learning 24,
pages 123-140, 1996.</td></tr>
</tbody>
</table>
</dd></dl>
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