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<li><a class="reference internal" href="#">Plotting Validation Curves</a></li>
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<div class="sphx-glr-example-title section" id="plotting-validation-curves">
<span id="sphx-glr-auto-examples-model-selection-plot-validation-curve-py"></span><h1>Plotting Validation Curves<a class="headerlink" href="#plotting-validation-curves" title="Permalink to this headline">¶</a></h1>
<p>In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. This is called underfitting. Medium values of gamma will result in high
values for both scores, i.e. the classifier is performing fairly well. If gamma
is too high, the classifier will overfit, which means that the training score
is good but the validation score is poor.</p>
<img alt="../../_images/sphx_glr_plot_validation_curve_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_plot_validation_curve_001.png" />
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</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">load_digits</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="View documentation for sklearn.model_selection.validation_curve"><span class="n">validation_curve</span></a>
<span class="n">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">load_digits</span></a><span class="p">()</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
<span class="n">param_range</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.logspace.html#numpy.logspace" title="View documentation for numpy.logspace"><span class="n">np</span><span class="o">.</span><span class="n">logspace</span></a><span class="p">(</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">train_scores</span><span class="p">,</span> <span class="n">test_scores</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="View documentation for sklearn.model_selection.validation_curve"><span class="n">validation_curve</span></a><span class="p">(</span>
<a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</span></a><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">param_name</span><span class="o">=</span><span class="s2">"gamma"</span><span class="p">,</span> <span class="n">param_range</span><span class="o">=</span><span class="n">param_range</span><span class="p">,</span>
<span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s2">"accuracy"</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">train_scores_mean</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html#numpy.mean" title="View documentation for numpy.mean"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">train_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">train_scores_std</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.std.html#numpy.std" title="View documentation for numpy.std"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">train_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">test_scores_mean</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html#numpy.mean" title="View documentation for numpy.mean"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">test_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">test_scores_std</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.std.html#numpy.std" title="View documentation for numpy.std"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">test_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"Validation Curve with SVM"</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="View documentation for matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="sa">r</span><span class="s2">"$\gamma$"</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="View documentation for matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">"Score"</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="View documentation for matplotlib.pyplot.ylim"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">)</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.semilogx.html#matplotlib.pyplot.semilogx" title="View documentation for matplotlib.pyplot.semilogx"><span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span></a><span class="p">(</span><span class="n">param_range</span><span class="p">,</span> <span class="n">train_scores_mean</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Training score"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"darkorange"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.fill_between.html#matplotlib.pyplot.fill_between" title="View documentation for matplotlib.pyplot.fill_between"><span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span></a><span class="p">(</span><span class="n">param_range</span><span class="p">,</span> <span class="n">train_scores_mean</span> <span class="o">-</span> <span class="n">train_scores_std</span><span class="p">,</span>
<span class="n">train_scores_mean</span> <span class="o">+</span> <span class="n">train_scores_std</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"darkorange"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.semilogx.html#matplotlib.pyplot.semilogx" title="View documentation for matplotlib.pyplot.semilogx"><span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span></a><span class="p">(</span><span class="n">param_range</span><span class="p">,</span> <span class="n">test_scores_mean</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Cross-validation score"</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"navy"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.fill_between.html#matplotlib.pyplot.fill_between" title="View documentation for matplotlib.pyplot.fill_between"><span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span></a><span class="p">(</span><span class="n">param_range</span><span class="p">,</span> <span class="n">test_scores_mean</span> <span class="o">-</span> <span class="n">test_scores_std</span><span class="p">,</span>
<span class="n">test_scores_mean</span> <span class="o">+</span> <span class="n">test_scores_std</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s2">"navy"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="View documentation for matplotlib.pyplot.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"best"</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p><strong>Total running time of the script:</strong> ( 0 minutes 13.416 seconds)</p>
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