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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_hastie_10_2</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-hastie-10-2">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_hastie_10_2</span></code></a></li>
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<div class="section" id="sklearn-datasets-make-hastie-10-2">
<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_hastie_10_2<a class="headerlink" href="#sklearn-datasets-make-hastie-10-2" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.datasets.make_hastie_10_2">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">make_hastie_10_2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n_samples</span><span class="o">=</span><span class="default_value">12000</span></em>, <em class="sig-param"><span class="o">*</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#L430"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_hastie_10_2" title="Permalink to this definition">¶</a></dt>
<dd><p>Generates data for binary classification used in
Hastie et al. 2009, Example 10.2.</p>
<p>The ten features are standard independent Gaussian and
the target <code class="docutils literal notranslate"><span class="pre">y</span></code> is defined by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">></span> <span class="mf">9.34</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</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=12000</span></dt><dd><p>The number of samples.</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 creation. 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, 10)</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>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles" title="sklearn.datasets.make_gaussian_quantiles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_gaussian_quantiles</span></code></a></dt><dd><p>A generalization of this dataset approach.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r144ef0107add-1"><span class="brackets">1</span></dt>
<dd><p>T. Hastie, R. Tibshirani and J. Friedman, “Elements of Statistical
Learning Ed. 2”, Springer, 2009.</p>
</dd>
</dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-make-hastie-10-2">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_hastie_10_2</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-hastie-10-2" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Illustration of the effect of different regularization strategies for Gradient Boosting. The ex..."><div class="figure align-default" id="id2">
<img alt="Gradient Boosting regularization" src="../../_images/sphx_glr_plot_gradient_boosting_regularization_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py"><span class="std std-ref">Gradient Boosting regularization</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example is based on Figure 10.2 from Hastie et al 2009 [1]_ and illustrates the difference..."><div class="figure align-default" id="id3">
<img alt="Discrete versus Real AdaBoost" src="../../_images/sphx_glr_plot_adaboost_hastie_10_2_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py"><span class="std std-ref">Discrete versus Real AdaBoost</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Gradient boosting is an ensembling technique where several weak learners (regression trees) are..."><div class="figure align-default" id="id4">
<img alt="Early stopping of Gradient Boosting" src="../../_images/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py"><span class="std std-ref">Early stopping of Gradient Boosting</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Multiple metric parameter search can be done by setting the scoring parameter to a list of metr..."><div class="figure align-default" id="id5">
<img alt="Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV" src="../../_images/sphx_glr_plot_multi_metric_evaluation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py"><span class="std std-ref">Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
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