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<li><a class="reference internal" href="#">Plotting Validation Curves</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-selection-plot-validation-curve-py"><span class="std std-ref">Go to the end</span></a>
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<section class="sphx-glr-example-title" 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="Link to this heading">¶</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 src="../../_images/sphx_glr_plot_validation_curve_001.png" srcset="../../_images/sphx_glr_plot_validation_curve_001.png" alt="Validation Curve for SVM with an RBF kernel" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">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="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">load_digits</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">ValidationCurveDisplay</span>
<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="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">SVC</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits" title="sklearn.datasets.load_digits" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><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">subset_mask</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.isin.html#numpy.isin" title="numpy.isin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">isin</span></a><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="c1"># binary classification: 1 vs 2</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">subset_mask</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">subset_mask</span><span class="p">]</span>
<span class="n">disp</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.ValidationCurveDisplay.html#sklearn.model_selection.ValidationCurveDisplay.from_estimator" title="sklearn.model_selection.ValidationCurveDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-model_selection-ValidationCurveDisplay sphx-glr-backref-type-py-method"><span class="n">ValidationCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
<a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="sklearn.svm.SVC" class="sphx-glr-backref-module-sklearn-svm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><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><a href="https://numpy.org/doc/stable/reference/generated/numpy.logspace.html#numpy.logspace" title="numpy.logspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><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">score_type</span><span class="o">=</span><span class="s2">"both"</span><span class="p">,</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">score_name</span><span class="o">=</span><span class="s2">"Accuracy"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">disp</span><span class="o">.</span><span class="n">ax_</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Validation Curve for SVM with an RBF kernel"</span><span class="p">)</span>
<span class="n">disp</span><span class="o">.</span><span class="n">ax_</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">"gamma (inverse radius of the RBF kernel)"</span><span class="p">)</span>
<span class="n">disp</span><span class="o">.</span><span class="n">ax_</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.554 seconds)</p>
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<p><a class="reference download internal" download="" href="../../_downloads/7996e584c563a930d174772f44af2089/plot_validation_curve.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_validation_curve.ipynb</span></code></a></p>
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<p><a class="reference download internal" download="" href="../../_downloads/d7ef5ff0bffa701d573ebc3ef124729a/plot_validation_curve.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_validation_curve.py</span></code></a></p>
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<p class="rubric">Related examples</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the effect of the parameters gamma and C of the Radial Basis Function ..."><img alt="" src="../../_images/sphx_glr_plot_rbf_parameters_thumb.png" />
<p><a class="reference internal" href="../svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py"><span class="std std-ref">RBF SVM parameters</span></a></p>
<div class="sphx-glr-thumbnail-title">RBF SVM parameters</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only ..."><img alt="" src="../../_images/sphx_glr_plot_iris_svc_thumb.png" />
<p><a class="reference internal" href="../svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py"><span class="std std-ref">Plot different SVM classifiers in the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot different SVM classifiers in the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example using IsolationForest for anomaly detection."><img alt="" src="../../_images/sphx_glr_plot_isolation_forest_thumb.png" />
<p><a class="reference internal" href="../ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py"><span class="std std-ref">IsolationForest example</span></a></p>
<div class="sphx-glr-thumbnail-title">IsolationForest example</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use KNeighborsClassifier. We train such a classifier on the iris data..."><img alt="" src="../../_images/sphx_glr_plot_classification_thumb.png" />
<p><a class="reference internal" href="../neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py"><span class="std std-ref">Nearest Neighbors Classification</span></a></p>
<div class="sphx-glr-thumbnail-title">Nearest Neighbors Classification</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example illustrating the approximation of the feature map of an RBF kernel."><img alt="" src="../../_images/sphx_glr_plot_kernel_approximation_thumb.png" />
<p><a class="reference internal" href="../miscellaneous/plot_kernel_approximation.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a></p>
<div class="sphx-glr-thumbnail-title">Explicit feature map approximation for RBF kernels</div>
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