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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
</ul>
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<li class="toctree-l1 has-children"><a class="reference internal" href="inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">4.2. Permutation feature importance</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">5. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">6. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
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  <section id="visualizations">
<span id="id1"></span><h1><span class="section-number">5. </span>Visualizations<a class="headerlink" href="#visualizations" title="Link to this heading">#</a></h1>
<p>Scikit-learn defines a simple API for creating visualizations for machine
learning. The key feature of this API is to allow for quick plotting and
visual adjustments without recalculation. We provide <code class="docutils literal notranslate"><span class="pre">Display</span></code> classes that
expose two methods for creating plots: <code class="docutils literal notranslate"><span class="pre">from_estimator</span></code> and
<code class="docutils literal notranslate"><span class="pre">from_predictions</span></code>. The <code class="docutils literal notranslate"><span class="pre">from_estimator</span></code> method will take a fitted estimator
and some data (<code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>) and create a <code class="docutils literal notranslate"><span class="pre">Display</span></code> object. Sometimes, we would
like to only compute the predictions once and one should use <code class="docutils literal notranslate"><span class="pre">from_predictions</span></code>
instead. In the following example, we plot a ROC curve for a fitted support
vector machine:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">RocCurveDisplay</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>

<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_wine</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="n">y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">==</span> <span class="mi">2</span>  <span class="c1"># make binary</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">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">svc</span> <span class="o">=</span> <span class="n">SVC</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="n">svc</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="n">svc_disp</span> <span class="o">=</span> <span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">svc</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>
</div>
<figure class="align-center">
<img alt="_images/visualizations-1.png" class="plot-directive" src="_images/visualizations-1.png" />
</figure>
<p>The returned <code class="docutils literal notranslate"><span class="pre">svc_disp</span></code> object allows us to continue using the already computed
ROC curve for SVC in future plots. In this case, the <code class="docutils literal notranslate"><span class="pre">svc_disp</span></code> is a
<a class="reference internal" href="modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">RocCurveDisplay</span></code></a> that stores the computed values as
attributes called <code class="docutils literal notranslate"><span class="pre">roc_auc</span></code>, <code class="docutils literal notranslate"><span class="pre">fpr</span></code>, and <code class="docutils literal notranslate"><span class="pre">tpr</span></code>. Be aware that we could get
the predictions from the support vector machine and then use <code class="docutils literal notranslate"><span class="pre">from_predictions</span></code>
instead of <code class="docutils literal notranslate"><span class="pre">from_estimator</span></code>. Next, we train a random forest classifier and plot
the previously computed roc curve again by using the <code class="docutils literal notranslate"><span class="pre">plot</span></code> method of the
<code class="docutils literal notranslate"><span class="pre">Display</span></code> object.</p>
<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">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>

<span class="n">rfc</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</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="n">rfc</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="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<span class="n">rfc_disp</span> <span class="o">=</span> <span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">rfc</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="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">svc_disp</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
</pre></div>
</div>
<figure class="align-center">
<img alt="_images/visualizations-2.png" class="plot-directive" src="_images/visualizations-2.png" />
</figure>
<p>Notice that we pass <code class="docutils literal notranslate"><span class="pre">alpha=0.8</span></code> to the plot functions to adjust the alpha
values of the curves.</p>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="auto_examples/miscellaneous/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-roc-curve-visualization-api-py"><span class="std std-ref">ROC Curve with Visualization API</span></a></p></li>
<li><p><a class="reference internal" href="auto_examples/miscellaneous/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-miscellaneous-plot-partial-dependence-visualization-api-py"><span class="std std-ref">Advanced Plotting With Partial Dependence</span></a></p></li>
<li><p><a class="reference internal" href="auto_examples/miscellaneous/plot_display_object_visualization.html#sphx-glr-auto-examples-miscellaneous-plot-display-object-visualization-py"><span class="std std-ref">Visualizations with Display Objects</span></a></p></li>
<li><p><a class="reference internal" href="auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py"><span class="std std-ref">Comparison of Calibration of Classifiers</span></a></p></li>
</ul>
<section id="available-plotting-utilities">
<h2><span class="section-number">5.1. </span>Available Plotting Utilities<a class="headerlink" href="#available-plotting-utilities" title="Link to this heading">#</a></h2>
<section id="display-objects">
<h3><span class="section-number">5.1.1. </span>Display Objects<a class="headerlink" href="#display-objects" title="Link to this heading">#</a></h3>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="modules/generated/sklearn.calibration.CalibrationDisplay.html#sklearn.calibration.CalibrationDisplay" title="sklearn.calibration.CalibrationDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibration.CalibrationDisplay</span></code></a>(prob_true, ...)</p></td>
<td><p>Calibration curve (also known as reliability diagram) visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay" title="sklearn.inspection.PartialDependenceDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inspection.PartialDependenceDisplay</span></code></a>(...[, ...])</p></td>
<td><p>Partial Dependence Plot (PDP).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html#sklearn.inspection.DecisionBoundaryDisplay" title="sklearn.inspection.DecisionBoundaryDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inspection.DecisionBoundaryDisplay</span></code></a>(*, xx0, ...)</p></td>
<td><p>Decisions boundary visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay" title="sklearn.metrics.ConfusionMatrixDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.ConfusionMatrixDisplay</span></code></a>(...[, ...])</p></td>
<td><p>Confusion Matrix visualization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay" title="sklearn.metrics.DetCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.DetCurveDisplay</span></code></a>(*, fpr, fnr[, ...])</p></td>
<td><p>DET curve visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="modules/generated/sklearn.metrics.PrecisionRecallDisplay.html#sklearn.metrics.PrecisionRecallDisplay" title="sklearn.metrics.PrecisionRecallDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.PrecisionRecallDisplay</span></code></a>(precision, ...)</p></td>
<td><p>Precision Recall visualization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="modules/generated/sklearn.metrics.PredictionErrorDisplay.html#sklearn.metrics.PredictionErrorDisplay" title="sklearn.metrics.PredictionErrorDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.PredictionErrorDisplay</span></code></a>(*, y_true, y_pred)</p></td>
<td><p>Visualization of the prediction error of a regression model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.RocCurveDisplay</span></code></a>(*, fpr, tpr[, ...])</p></td>
<td><p>ROC Curve visualization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="modules/generated/sklearn.model_selection.LearningCurveDisplay.html#sklearn.model_selection.LearningCurveDisplay" title="sklearn.model_selection.LearningCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.LearningCurveDisplay</span></code></a>(*, ...)</p></td>
<td><p>Learning Curve visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="modules/generated/sklearn.model_selection.ValidationCurveDisplay.html#sklearn.model_selection.ValidationCurveDisplay" title="sklearn.model_selection.ValidationCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_selection.ValidationCurveDisplay</span></code></a>(*, ...)</p></td>
<td><p>Validation Curve visualization.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>
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