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example_for_paper.html
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example_for_paper.html
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<!DOCTYPE html>
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<title><no title> — MRCpy 0.1.0 documentation</title>
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<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-example-for-paper-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
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<p class="sphx-glr-example-title" id="sphx-glr-auto-examples-example-for-paper-py">A simple example for the paper</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">MRCpy</span> <span class="kn">import</span> <span class="n">MRC</span>
<span class="kn">from</span> <span class="nn">MRCpy.datasets</span> <span class="kn">import</span> <span class="n">load_mammographic</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="c1"># Load the mammographic dataset</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">load_mammographic</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Split the data into training and testing</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="c1"># Create the MRC classifier using default loss (0-1)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">MRC</span><span class="p">()</span>
<span class="c1"># Fit the classifier on the training data</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Bounds on the classification error (only for MRC)</span>
<span class="n">lower_error</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">get_lower_bound</span><span class="p">()</span>
<span class="n">upper_error</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">upper_</span>
<span class="c1"># Compute the accuracy on the test set</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</pre></div>
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