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<li><a class="reference internal" href="#">Digits Classification Exercise</a></li>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-exercises-plot-digits-classification-exercise-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
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<div class="sphx-glr-example-title section" id="digits-classification-exercise">
<span id="sphx-glr-auto-examples-exercises-plot-digits-classification-exercise-py"></span><h1>Digits Classification Exercise<a class="headerlink" href="#digits-classification-exercise" title="Permalink to this headline">¶</a></h1>
<p>A tutorial exercise regarding the use of classification techniques on
the Digits dataset.</p>
<p>This exercise is used in the <a class="reference internal" href="../../tutorial/statistical_inference/supervised_learning.html#clf-tut"><span class="std std-ref">Classification</span></a> part of the
<a class="reference internal" href="../../tutorial/statistical_inference/supervised_learning.html#supervised-learning-tut"><span class="std std-ref">Supervised learning: predicting an output variable from high-dimensional observations</span></a> section of the
<a class="reference internal" href="../../tutorial/statistical_inference/index.html#stat-learn-tut-index"><span class="std std-ref">A tutorial on statistical-learning for scientific data processing</span></a>.</p>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>KNN score: 0.961111
LogisticRegression score: 0.933333
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">linear_model</span>
<span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="View documentation for sklearn.datasets.load_digits"><span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span></a><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="n">X_digits</span> <span class="o">=</span> <span class="n">X_digits</span> <span class="o">/</span> <span class="n">X_digits</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_digits</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_digits</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="o">.</span><span class="mi">9</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">)]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y_digits</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="o">.</span><span class="mi">9</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">)]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X_digits</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="o">.</span><span class="mi">9</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">):]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">y_digits</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="o">.</span><span class="mi">9</span> <span class="o">*</span> <span class="n">n_samples</span><span class="p">):]</span>
<span class="n">knn</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier" title="View documentation for sklearn.neighbors.KNeighborsClassifier"><span class="n">neighbors</span><span class="o">.</span><span class="n">KNeighborsClassifier</span></a><span class="p">()</span>
<span class="n">logistic</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="View documentation for sklearn.linear_model.LogisticRegression"><span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'KNN score: </span><span class="si">%f</span><span class="s1">'</span> <span class="o">%</span> <span class="n">knn</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="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>
<span class="nb">print</span><span class="p">(</span><span class="s1">'LogisticRegression score: </span><span class="si">%f</span><span class="s1">'</span>
<span class="o">%</span> <span class="n">logistic</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="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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