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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.fetch_mldata</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-mldata">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_mldata</span></code></a></li>
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<div class="section" id="sklearn-datasets-fetch-mldata">
<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>.fetch_mldata<a class="headerlink" href="#sklearn-datasets-fetch-mldata" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.fetch_mldata">
<code class="descclassname">sklearn.datasets.</code><code class="descname">fetch_mldata</code><span class="sig-paren">(</span><em>dataname</em>, <em>target_name='label'</em>, <em>data_name='data'</em>, <em>transpose_data=True</em>, <em>data_home=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/datasets/mldata.py#L49"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_mldata" title="Permalink to this definition">¶</a></dt>
<dd><p>Fetch an mldata.org data set</p>
<p>If the file does not exist yet, it is downloaded from mldata.org .</p>
<p>mldata.org does not have an enforced convention for storing data or
naming the columns in a data set. The default behavior of this function
works well with the most common cases:</p>
<blockquote>
<div><ol class="arabic simple">
<li>data values are stored in the column ‘data’, and target values in the
column ‘label’</li>
<li>alternatively, the first column stores target values, and the second
data values</li>
<li>the data array is stored as <cite>n_features x n_samples</cite> , and thus needs
to be transposed to match the <cite>sklearn</cite> standard</li>
</ol>
</div></blockquote>
<p>Keyword arguments allow to adapt these defaults to specific data sets
(see parameters <cite>target_name</cite>, <cite>data_name</cite>, <cite>transpose_data</cite>, and
the examples below).</p>
<p>mldata.org data sets may have multiple columns, which are stored in the
Bunch object with their original name.</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>dataname</strong> : str</p>
<blockquote>
<div><p>Name of the data set on mldata.org,
e.g.: “leukemia”, “Whistler Daily Snowfall”, etc.
The raw name is automatically converted to a mldata.org URL .</p>
</div></blockquote>
<p><strong>target_name</strong> : optional, default: ‘label’</p>
<blockquote>
<div><p>Name or index of the column containing the target values.</p>
</div></blockquote>
<p><strong>data_name</strong> : optional, default: ‘data’</p>
<blockquote>
<div><p>Name or index of the column containing the data.</p>
</div></blockquote>
<p><strong>transpose_data</strong> : optional, default: True</p>
<blockquote>
<div><p>If True, transpose the downloaded data array.</p>
</div></blockquote>
<p><strong>data_home</strong> : optional, default: None</p>
<blockquote>
<div><p>Specify another download and cache folder for the data sets. By default
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> : Bunch</p>
<blockquote class="last">
<div><p>Dictionary-like object, the interesting attributes are:
‘data’, the data to learn, ‘target’, the classification labels,
‘DESCR’, the full description of the dataset, and
‘COL_NAMES’, the original names of the dataset columns.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<p>Load the ‘iris’ dataset from mldata.org:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets.mldata</span> <span class="k">import</span> <span class="n">fetch_mldata</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">tempfile</span>
<span class="gp">>>> </span><span class="n">test_data_home</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span><span class="p">()</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iris</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s1">'iris'</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="n">test_data_home</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(150,)</span>
<span class="gp">>>> </span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(150, 4)</span>
</pre></div>
</div>
<p>Load the ‘leukemia’ dataset from mldata.org, which needs to be transposed
to respects the scikit-learn axes convention:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">leuk</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s1">'leukemia'</span><span class="p">,</span> <span class="n">transpose_data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">data_home</span><span class="o">=</span><span class="n">test_data_home</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">leuk</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(72, 7129)</span>
</pre></div>
</div>
<p>Load an alternative ‘iris’ dataset, which has different names for the
columns:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iris2</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s1">'datasets-UCI iris'</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">data_name</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="n">test_data_home</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris3</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s1">'datasets-UCI iris'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">target_name</span><span class="o">=</span><span class="s1">'class'</span><span class="p">,</span> <span class="n">data_name</span><span class="o">=</span><span class="s1">'double0'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">data_home</span><span class="o">=</span><span class="n">test_data_home</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">shutil</span>
<span class="gp">>>> </span><span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">test_data_home</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-fetch-mldata">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.fetch_mldata</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-mldata" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example is based on Section 5.4.3 of "Gaussian Processes for Machine Learning" [RW2006]. I..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_gpr_co2_thumb.png" src="../../_images/sphx_glr_plot_gpr_co2_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py"><span class="std std-ref">Gaussian process regression (GPR) on Mauna Loa CO2 data.</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits c..."><div class="figure" id="id2">
<img alt="../../_images/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png" src="../../_images/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py"><span class="std std-ref">MNIST classfification using multinomial logistic + L1</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="For this example we will use the `yeast <http://mldata.org/repository/data/viewslug/yeast>`_ da..."><div class="figure" id="id3">
<img alt="../../_images/sphx_glr_plot_classifier_chain_yeast_thumb.png" src="../../_images/sphx_glr_plot_classifier_chain_yeast_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/multioutput/plot_classifier_chain_yeast.html#sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py"><span class="std std-ref">Classifier Chain</span></a></span></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Sometimes looking at the learned coefficients of a neural network can provide insight into the ..."><div class="figure" id="id4">
<img alt="../../_images/sphx_glr_plot_mnist_filters_thumb.png" src="../../_images/sphx_glr_plot_mnist_filters_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py"><span class="std std-ref">Visualization of MLP weights on MNIST</span></a></span></p>
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