dev/visualizations.html


<!DOCTYPE html>


<html lang="en" data-content_root="./" >

  <head>
    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="5. Visualizations" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://scikit-learn/stable/visualizations.html" />
<meta property="og:site_name" content="scikit-learn" />
<meta property="og:description" content="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 provi..." />
<meta property="og:image" content="https://scikit-learn/stable/_images/visualizations-1.png" />
<meta property="og:image:alt" content="scikit-learn" />
<meta name="description" content="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 provi..." />

    <title>5. Visualizations &#8212; scikit-learn 1.6.dev0 documentation</title>
  
  
  <script data-cfasync="false">
    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
    document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
  </script>
  
  <!-- Loaded before other Sphinx assets -->
  <link href="_static/styles/theme.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="_static/styles/bootstrap.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />

  
  <link href="_static/vendor/fontawesome/6.5.2/css/all.min.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
  <link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />

    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=a746c00c" />
    <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
    <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
    <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
    <link rel="stylesheet" type="text/css" href="_static/jupyterlite_sphinx.css?v=ca70e7f1" />
    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
    <link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=87e54e7c" />
    <link rel="stylesheet" type="text/css" href="_static/styles/colors.css?v=cc94ab7d" />
    <link rel="stylesheet" type="text/css" href="_static/styles/custom.css?v=e4cb1417" />
  
  <!-- Pre-loaded scripts that we'll load fully later -->
  <link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=dfe6caa3a7d634c4db9b" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
  <script src="_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>

    <script src="_static/documentation_options.js?v=d875d36e"></script>
    <script src="_static/doctools.js?v=9a2dae69"></script>
    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
    <script src="_static/clipboard.min.js?v=a7894cd8"></script>
    <script src="_static/copybutton.js?v=97f0b27d"></script>
    <script src="_static/jupyterlite_sphinx.js?v=d6bdf5f8"></script>
    <script src="_static/design-tabs.js?v=f930bc37"></script>
    <script data-domain="scikit-learn.org" defer="defer" src="https://views.scientific-python.org/js/script.js"></script>
    <script>DOCUMENTATION_OPTIONS.pagename = 'visualizations';</script>
    <script>
        DOCUMENTATION_OPTIONS.theme_version = '0.15.4';
        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://scikit-learn.org/dev/_static/versions.json';
        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '1.6.dev0';
        DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
        </script>
    <script src="_static/scripts/dropdown.js?v=e2048168"></script>
    <script src="_static/scripts/version-switcher.js?v=a6dd8357"></script>
    <link rel="icon" href="_static/favicon.ico"/>
    <link rel="author" title="About these documents" href="about.html" />
    <link rel="search" title="Search" href="search.html" />
    <link rel="next" title="6. Dataset transformations" href="data_transforms.html" />
    <link rel="prev" title="4.2. Permutation feature importance" href="modules/permutation_importance.html" />
  <meta name="viewport" content="width=device-width, initial-scale=1"/>
  <meta name="docsearch:language" content="en"/>
  </head>
  
  
  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">

  
  <div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
  
  <div id="pst-scroll-pixel-helper"></div>
  
  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
    <i class="fa-solid fa-arrow-up"></i>Back to top</button>

  
  <input type="checkbox"
          class="sidebar-toggle"
          id="pst-primary-sidebar-checkbox"/>
  <label class="overlay overlay-primary" for="pst-primary-sidebar-checkbox"></label>
  
  <input type="checkbox"
          class="sidebar-toggle"
          id="pst-secondary-sidebar-checkbox"/>
  <label class="overlay overlay-secondary" for="pst-secondary-sidebar-checkbox"></label>
  
  <div class="search-button__wrapper">
    <div class="search-button__overlay"></div>
    <div class="search-button__search-container">
<form class="bd-search d-flex align-items-center"
      action="search.html"
      method="get">
  <i class="fa-solid fa-magnifying-glass"></i>
  <input type="search"
         class="form-control"
         name="q"
         id="search-input"
         placeholder="Search the docs ..."
         aria-label="Search the docs ..."
         autocomplete="off"
         autocorrect="off"
         autocapitalize="off"
         spellcheck="false"/>
  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form></div>
  </div>

  <div class="pst-async-banner-revealer d-none">
  <aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>

  
    <header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
  <button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
    <span class="fa-solid fa-bars"></span>
  </button>
  
  
  <div class=" navbar-header-items__start">
    
      <div class="navbar-item">

  
<a class="navbar-brand logo" href="index.html">
  
  
    <img src="_static/scikit-learn-logo-small.png" class="logo__image only-light" alt="scikit-learn homepage"/>
    <script>document.write(`<img src="_static/scikit-learn-logo-small.png" class="logo__image only-dark" alt="scikit-learn homepage"/>`);</script>
  
  
</a></div>
    
  </div>
  
  <div class=" navbar-header-items">
    
    <div class="me-auto navbar-header-items__center">
      
        <div class="navbar-item">
<nav>
  <ul class="bd-navbar-elements navbar-nav">
    
<li class="nav-item ">
  <a class="nav-link nav-internal" href="install.html">
    Install
  </a>
</li>


<li class="nav-item current active">
  <a class="nav-link nav-internal" href="user_guide.html">
    User Guide
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="api/index.html">
    API
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="auto_examples/index.html">
    Examples
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
    Community
  </a>
</li>

            <li class="nav-item dropdown">
                <button class="btn dropdown-toggle nav-item" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-controls="pst-nav-more-links">
                    More
                </button>
                <ul id="pst-nav-more-links" class="dropdown-menu">
                    
<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="getting_started.html">
    Getting Started
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="whats_new.html">
    Release History
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="glossary.html">
    Glossary
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="developers/index.html">
    Development
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="faq.html">
    FAQ
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="support.html">
    Support
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="related_projects.html">
    Related Projects
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="roadmap.html">
    Roadmap
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="governance.html">
    Governance
  </a>
</li>


<li class=" ">
  <a class="nav-link dropdown-item nav-internal" href="about.html">
    About us
  </a>
</li>

                </ul>
            </li>
            
  </ul>
</nav></div>
      
    </div>
    
    
    <div class="navbar-header-items__end">
      
        <div class="navbar-item navbar-persistent--container">
          

<script>
document.write(`
  <button class="btn btn-sm pst-navbar-icon search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
    <i class="fa-solid fa-magnifying-glass fa-lg"></i>
  </button>
`);
</script>
        </div>
      
      
        <div class="navbar-item">

<script>
document.write(`
  <button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
    <i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light"></i>
    <i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark"></i>
    <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"></i>
  </button>
`);
</script></div>
      
        <div class="navbar-item"><ul class="navbar-icon-links"
    aria-label="Icon Links">
        <li class="nav-item">
          
          
          <a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
            <span class="sr-only">GitHub</span></a>
        </li>
</ul></div>
      
        <div class="navbar-item">
<script>
document.write(`
  <div class="version-switcher__container dropdown">
    <button id="pst-version-switcher-button-2"
      type="button"
      class="version-switcher__button btn btn-sm dropdown-toggle"
      data-bs-toggle="dropdown"
      aria-haspopup="listbox"
      aria-controls="pst-version-switcher-list-2"
      aria-label="Version switcher list"
    >
      Choose version  <!-- this text may get changed later by javascript -->
      <span class="caret"></span>
    </button>
    <div id="pst-version-switcher-list-2"
      class="version-switcher__menu dropdown-menu list-group-flush py-0"
      role="listbox" aria-labelledby="pst-version-switcher-button-2">
      <!-- dropdown will be populated by javascript on page load -->
    </div>
  </div>
`);
</script></div>
      
    </div>
    
  </div>
  
  
    <div class="navbar-persistent--mobile">

<script>
document.write(`
  <button class="btn btn-sm pst-navbar-icon search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
    <i class="fa-solid fa-magnifying-glass fa-lg"></i>
  </button>
`);
</script>
    </div>
  

    <button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
      <span class="fa-solid fa-outdent"></span>
    </button>
  
</div>

    </header>
  

  <div class="bd-container">
    <div class="bd-container__inner bd-page-width">
      
      
      <div class="bd-sidebar-primary bd-sidebar">
        

  <div class="sidebar-header-items sidebar-primary__section">
    
    
      <div class="sidebar-header-items__center">
        
          
            <div class="navbar-item">
<nav>
  <ul class="bd-navbar-elements navbar-nav">
    
<li class="nav-item ">
  <a class="nav-link nav-internal" href="install.html">
    Install
  </a>
</li>


<li class="nav-item current active">
  <a class="nav-link nav-internal" href="user_guide.html">
    User Guide
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="api/index.html">
    API
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="auto_examples/index.html">
    Examples
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
    Community
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="getting_started.html">
    Getting Started
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="whats_new.html">
    Release History
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="glossary.html">
    Glossary
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="developers/index.html">
    Development
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="faq.html">
    FAQ
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="support.html">
    Support
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="related_projects.html">
    Related Projects
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="roadmap.html">
    Roadmap
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="governance.html">
    Governance
  </a>
</li>


<li class="nav-item ">
  <a class="nav-link nav-internal" href="about.html">
    About us
  </a>
</li>

  </ul>
</nav></div>
          
        
      </div>
    
    
      <div class="sidebar-header-items__end">
        
          <div class="navbar-item">

<script>
document.write(`
  <button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
    <i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light"></i>
    <i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark"></i>
    <i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"></i>
  </button>
`);
</script></div>
        
          <div class="navbar-item"><ul class="navbar-icon-links"
    aria-label="Icon Links">
        <li class="nav-item">
          
          
          <a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
            <span class="sr-only">GitHub</span></a>
        </li>
</ul></div>
        
          <div class="navbar-item">
<script>
document.write(`
  <div class="version-switcher__container dropdown">
    <button id="pst-version-switcher-button-3"
      type="button"
      class="version-switcher__button btn btn-sm dropdown-toggle"
      data-bs-toggle="dropdown"
      aria-haspopup="listbox"
      aria-controls="pst-version-switcher-list-3"
      aria-label="Version switcher list"
    >
      Choose version  <!-- this text may get changed later by javascript -->
      <span class="caret"></span>
    </button>
    <div id="pst-version-switcher-list-3"
      class="version-switcher__menu dropdown-menu list-group-flush py-0"
      role="listbox" aria-labelledby="pst-version-switcher-button-3">
      <!-- dropdown will be populated by javascript on page load -->
    </div>
  </div>
`);
</script></div>
        
      </div>
    
  </div>
  
    <div class="sidebar-primary-items__start sidebar-primary__section">
        <div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
     aria-label="Section Navigation">
  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
  <div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
<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>
</details></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<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>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="datasets.html">7. Dataset loading utilities</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="datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="computing.html">8. Computing with scikit-learn</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="computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">11. Dispatching</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/array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="presentations.html">13. External Resources, Videos and Talks</a></li>
</ul>
</div>
</nav></div>
    </div>
  
  
  <div class="sidebar-primary-items__end sidebar-primary__section">
  </div>
  
  <div id="rtd-footer-container"></div>


      </div>
      
      <main id="main-content" class="bd-main" role="main">
        
        
          <div class="bd-content">
            <div class="bd-article-container">
              
              <div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
  
    <div class="header-article-items__start">
      
        <div class="header-article-item">


<nav aria-label="Breadcrumb" class="d-print-none">
  <ul class="bd-breadcrumbs">
    
    <li class="breadcrumb-item breadcrumb-home">
      <a href="index.html" class="nav-link" aria-label="Home">
        <i class="fa-solid fa-home"></i>
      </a>
    </li>
    
    <li class="breadcrumb-item"><a href="user_guide.html" class="nav-link">User Guide</a></li>
    
    <li class="breadcrumb-item active" aria-current="page"><span...</li>
  </ul>
</nav>
</div>
      
    </div>
  
  
</div>
</div>
              
              
<div id="searchbox"></div>
                <article class="bd-article">
                  
  <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>
</section>


                </article>
              
              
                <footer class="bd-footer-article">
                  <div class="footer-article-items footer-article__inner">
  
    <div class="footer-article-item">
<div class="prev-next-area">
    <a class="left-prev"
       href="modules/permutation_importance.html"
       title="previous page">
      <i class="fa-solid fa-angle-left"></i>
      <div class="prev-next-info">
        <p class="prev-next-subtitle">previous</p>
        <p class="prev-next-title"><span class="section-number">4.2. </span>Permutation feature importance</p>
      </div>
    </a>
    <a class="right-next"
       href="data_transforms.html"
       title="next page">
      <div class="prev-next-info">
        <p class="prev-next-subtitle">next</p>
        <p class="prev-next-title"><span class="section-number">6. </span>Dataset transformations</p>
      </div>
      <i class="fa-solid fa-angle-right"></i>
    </a>
</div></div>
  
</div>
                </footer>
              
              
            </div>
            
            
                <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">


  <div class="sidebar-secondary-item">
<div
    id="pst-page-navigation-heading-2"
    class="page-toc tocsection onthispage">
    <i class="fa-solid fa-list"></i> On this page
  </div>
  <nav class="bd-toc-nav page-toc" aria-labelledby="pst-page-navigation-heading-2">
    <ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#available-plotting-utilities">5.1. Available Plotting Utilities</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#display-objects">5.1.1. Display Objects</a></li>
</ul>
</li>
</ul>
  </nav></div>

  <div class="sidebar-secondary-item">

  <div class="tocsection sourcelink">
    <a href="_sources/visualizations.rst.txt">
      <i class="fa-solid fa-file-lines"></i> Show Source
    </a>
  </div>
</div>

</div></div>
              
            
          </div>
          <footer class="bd-footer-content">
            
          </footer>
        
      </main>
    </div>
  </div>
  
  <!-- Scripts loaded after <body> so the DOM is not blocked -->
  <script src="_static/scripts/bootstrap.js?digest=dfe6caa3a7d634c4db9b"></script>
<script src="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b"></script>

  <footer class="bd-footer">
<div class="bd-footer__inner bd-page-width">
  
    <div class="footer-items__start">
      
        <div class="footer-item">

  <p class="copyright">
    
      © Copyright 2007 - 2024, scikit-learn developers (BSD License).
      <br/>
    
  </p>
</div>
      
    </div>
  
  
</div>

  </footer>
  </body>
</html>