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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>.fetch_species_distributions</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-species-distributions">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_species_distributions</span></code></a></li>
</ul>
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<section id="sklearn-datasets-fetch-species-distributions">
<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>.fetch_species_distributions<a class="headerlink" href="#sklearn-datasets-fetch-species-distributions" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_species_distributions">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_species_distributions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/6cb2c5237/sklearn/datasets/_species_distributions.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_species_distributions" title="Permalink to this definition">¶</a></dt>
<dd><p>Loader for species distribution dataset from Phillips et. al. (2006)</p>
<p>Read more in the <a class="reference internal" href="../../datasets.html#datasets"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>data_home</strong><span class="classifier">str, default=None</span></dt><dd><p>Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</dd>
<dt><strong>download_if_missing</strong><span class="classifier">bool, default=True</span></dt><dd><p>If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>data</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl class="simple">
<dt>coverages<span class="classifier">array, shape = [14, 1592, 1212]</span></dt><dd><p>These represent the 14 features measured
at each point of the map grid.
The latitude/longitude values for the grid are discussed below.
Missing data is represented by the value -9999.</p>
</dd>
<dt>train<span class="classifier">record array, shape = (1624,)</span></dt><dd><p>The training points for the data. Each point has three fields:</p>
<ul class="simple">
<li><p>train[‘species’] is the species name</p></li>
<li><p>train[‘dd long’] is the longitude, in degrees</p></li>
<li><p>train[‘dd lat’] is the latitude, in degrees</p></li>
</ul>
</dd>
<dt>test<span class="classifier">record array, shape = (620,)</span></dt><dd><p>The test points for the data. Same format as the training data.</p>
</dd>
<dt>Nx, Ny<span class="classifier">integers</span></dt><dd><p>The number of longitudes (x) and latitudes (y) in the grid</p>
</dd>
<dt>x_left_lower_corner, y_left_lower_corner<span class="classifier">floats</span></dt><dd><p>The (x,y) position of the lower-left corner, in degrees</p>
</dd>
<dt>grid_size<span class="classifier">float</span></dt><dd><p>The spacing between points of the grid, in degrees</p>
</dd>
</dl>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).</p>
<p>The two species are:</p>
<ul class="simple">
<li><p><a class="reference external" href="http://www.iucnredlist.org/details/3038/0">“Bradypus variegatus”</a> ,
the Brown-throated Sloth.</p></li>
<li><p><a class="reference external" href="http://www.iucnredlist.org/details/13408/0">“Microryzomys minutus”</a> ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.</p></li>
<li><p>For an example of using this dataset with scikit-learn, see
<a class="reference internal" href="../../auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">examples/applications/plot_species_distribution_modeling.py</span></a>.</p></li>
</ul>
<p class="rubric">References</p>
<ul class="simple">
<li><p><a class="reference external" href="http://rob.schapire.net/papers/ecolmod.pdf">“Maximum entropy modeling of species geographic distributions”</a>
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.</p></li>
</ul>
</dd></dl>
<section id="examples-using-sklearn-datasets-fetch-species-distributions">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_species_distributions</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-species-distributions" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Modeling species' geographic distributions is an important problem in conservation biology. In ..."><figure class="align-default" id="id1">
<img alt="Species distribution modeling" src="../../_images/sphx_glr_plot_species_distribution_modeling_thumb.png" />
<figcaption>
<p><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py"><span class="std std-ref">Species distribution modeling</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example does not perform any learning over the data (see sphx_glr_auto_examples_applicatio..."><figure class="align-default" id="id2">
<img alt="Kernel Density Estimate of Species Distributions" src="../../_images/sphx_glr_plot_species_kde_thumb.png" />
<figcaption>
<p><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py"><span class="std std-ref">Kernel Density Estimate of Species Distributions</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
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