
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/miscellaneous/plot_roc_curve_visualization_api.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py:


================================
ROC Curve with Visualization API
================================
Scikit-learn defines a simple API for creating visualizations for machine
learning. The key features of this API is to allow for quick plotting and
visual adjustments without recalculation. In this example, we will demonstrate
how to use the visualization API by comparing ROC curves.

.. GENERATED FROM PYTHON SOURCE LINES 11-15

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 16-20

Load Data and Train an SVC
--------------------------
First, we load the wine dataset and convert it to a binary classification
problem. Then, we train a support vector classifier on a training dataset.

.. GENERATED FROM PYTHON SOURCE LINES 20-35

.. code-block:: Python

    import matplotlib.pyplot as plt

    from sklearn.datasets import load_wine
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import RocCurveDisplay
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC

    X, y = load_wine(return_X_y=True)
    y = y == 2

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    svc = SVC(random_state=42)
    svc.fit(X_train, y_train)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>.sk-global {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: #000;
      --sklearn-color-text-muted: #666;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;
    }

    .sk-global.light {
      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: black;
      --sklearn-color-background: white;
      --sklearn-color-border-box: black;
      --sklearn-color-icon: #696969;
    }

    .sk-global.dark {
      --sklearn-color-text-on-default-background: white;
      --sklearn-color-background: #111;
      --sklearn-color-border-box: white;
      --sklearn-color-icon: #878787;
    }

    .sk-global {
      color: var(--sklearn-color-text);
    }

    .sk-global pre {
      padding: 0;
    }

    .sk-global input.sk-hidden--visually {
      border: 0;
      clip-path: inset(100%);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
    }

    .sk-global div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
      padding-bottom: 0.4em;
      background-color: var(--sklearn-color-background);
    }

    .sk-global div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    .sk-global div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    .sk-global div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    .sk-global div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    .sk-global div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    .sk-global div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    .sk-global div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    .sk-global div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    .sk-global div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    clickable and can be expanded/collapsed.
    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    .sk-global div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    .sk-global label.sk-toggleable__label {
      cursor: pointer;
      display: flex;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
      align-items: center;
      justify-content: center;
      gap: 0.5em;
    }

    .sk-global label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    .sk-global label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    .sk-global label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    .sk-global div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    .sk-global div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    .sk-global div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    .sk-global div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    .sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    .sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    .sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    .sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    .sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    .sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    .sk-global div.sk-label label.sk-toggleable__label,
    .sk-global div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    .sk-global div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    .sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    .sk-global div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    .sk-global div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    .sk-global div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    .sk-global div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    .sk-global div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    .sk-global div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-3) 1pt solid;
      color: var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3) 1pt solid;
      color: var(--sklearn-color-fitted-level-3);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-unfitted-level-0);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-fitted-level-0);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    .sk-global a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    .sk-global a.estimator_doc_link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    .sk-global a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    .sk-global a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }

    .sk-top-container.sk-global {
      /* pydata-sphinx-theme hides overflow, so scrolling is disabled.
       We need to set it to !important and add tabindex="0" in the HTML
       to allow keyboard-only users to navigate the display. */
      overflow-x: scroll !important;
      max-width: 100%;
    }

    .estimator-table {
        font-family: monospace;
    }

    .estimator-table summary {
        padding: .5rem;
        cursor: pointer;
    }

    .estimator-table summary::marker {
        font-size: 0.7rem;
    }

    .estimator-table details[open] {
        padding-left: 0.1rem;
        padding-right: 0.1rem;
        padding-bottom: 0.3rem;
    }

    .estimator-table .parameters-table {
        margin-left: auto !important;
        margin-right: auto !important;
        margin-top: 0;
    }

    .estimator-table .parameters-table tr:nth-child(odd) {
        background-color: #fff;
    }

    .estimator-table .parameters-table tr:nth-child(even) {
        background-color: #f6f6f6;
    }

    .estimator-table .parameters-table tr:hover td {
        background-color: #e0e0e0;
    }

    .estimator-table table :is(td, th) {
        border: 1px solid rgba(106, 105, 104, 0.232);
    }

    /*
        `table td`is set in notebook with right text-align.
        We need to overwrite it.
    */
    .estimator-table table td.param {
        text-align: left;
        position: relative;
        padding: 0;
    }

    .user-set td {
        color:rgb(255, 94, 0);
        text-align: left !important;
    }

    .user-set td.value {
        color:rgb(255, 94, 0);
        background-color: transparent;
    }

    .default td, .estimator-table th {
        color: black;
        text-align: left !important;
    }

    .user-set td i,
    .default td i {
        color: black;
    }

    td.fitted-att-type {
        white-space: preserve nowrap;
    }

    /*
        Styles for parameter documentation links
        We need styling for visited so jupyter doesn't overwrite it
    */
    a.param-doc-link,
    a.param-doc-link:link,
    a.param-doc-link:visited {
        text-decoration: underline dashed;
        text-underline-offset: .3em;
        color: inherit;
        display: block;
        padding: .5em;
    }

    @supports(anchor-name: --doc-link) {
        a.param-doc-link,
        a.param-doc-link:link,
        a.param-doc-link:visited {
        anchor-name: --doc-link;
        }
    }

    /* "hack" to make the entire area of the cell containing the link clickable */
    a.param-doc-link::before {
        position: absolute;
        content: "";
        inset: 0;
    }

    .param-doc-description {
        display: none;
        position: absolute;
        z-index: 9999;
        left: 0;
        padding: .5ex;
        margin-left: 1.5em;
        color: var(--sklearn-color-text);
        box-shadow: .3em .3em .4em #999;
        width: max-content;
        text-align: left;
        max-height: 10em;
        overflow-y: auto;

        /* unfitted */
        background: var(--sklearn-color-unfitted-level-0);
        border: thin solid var(--sklearn-color-unfitted-level-3);
    }

    @supports(position-area: center right) {
        .param-doc-description {
        position-area: center right;
        position: fixed;
        margin-left: 0;
        }
    }

    /* Fitted state for parameter tooltips */
    .fitted .param-doc-description {
        /* fitted */
        background: var(--sklearn-color-fitted-level-0);
        border: thin solid var(--sklearn-color-fitted-level-3);
    }

    .param-doc-link:hover .param-doc-description {
        display: block;
    }

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }

    .features {
      font-family: monospace;
      cursor: pointer;
      background-color: var(--sklearn-color-unfitted-level-0);
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: .20em;
      margin-bottom: 0.5em;
      font-size: inherit; /* Needed for jupyter */
    }

    .features.fitted {
      background-color: var(--sklearn-color-fitted-level-0);
    }

    .features summary {
      cursor: pointer;
      display: flex;
      margin-bottom: 0;
      text-align: center;
      align-items: center;
      justify-content: center;
      gap: 0.5em;
      padding: .25em;
    }

    .features details[open] > summary {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
      border-radius: .20em 0 0 0;
    }

    .features.fitted details[open] > summary {
      background-color: var(--sklearn-color-fitted-level-2);
      border-radius: .20em 0 0 0;
    }

    .features details > summary .arrow::before {
      content: "▸";
      color: grey;
    }

    .features details[open] > summary .arrow::before {
      content: "▾";
    }

    .features details:hover > summary {
      margin: 0;
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    .features.fitted details:hover > summary {
      margin: 0;
      background-color: var(--sklearn-color-fitted-level-2);
    }

    .features .features-container {
      max-width: 15em;
      max-height: 10em;
      overflow: auto;
      scrollbar-width: thin;
      padding: .25em 0.1rem;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 0 0 .5em .5em;
    }

    .features.fitted .features-container {
      background-color: var(--sklearn-color-fitted-level-0);
    }

    .features .image-container {
      block-size: 1em;
      inline-size: 1em;
      padding: 0;
      margin: 0%;
      display: flex;
      justify-content: center;
      align-items: center;
    }

    .features .copy-paste-icon {
      background-size: 1em 1em;
      width: 1em;
      height: 1em;
      filter: grayscale(100%) opacity(60%);
    }

    .features .features-container table {
      width: 100%;
      margin: 0.01em;
    }

    .features .features-container table tr:nth-child(odd) {
      background-color: #fff;
    }

    .features .features-container table tr:nth-child(even) {
      background-color: #f6f6f6;
    }

    .features .features-container table tr:hover {
      background-color: #e0e0e0;
    }

    .features .features-container table {
      table-layout: inherit;
    }

    .features .features-container table td {
      text-align: left;
      padding: 0 0.5em;
      border: 1px solid rgba(106, 105, 104, 0.232);
      white-space: nowrap;
      color: var(--sklearn-color-text);
    }

    .total_features {
      display: flex;
      justify-content: center;
      margin-top: 0.5em;
    }
    </style><body><div id="sk-container-id-38" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-133" type="checkbox" checked><label for="sk-estimator-id-133" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-random_state;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone">
                random_state
                <span class="param-doc-description"
                style="position-anchor: --doc-link-random_state;">
                random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>probability estimates. Ignored when `probability` is False.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>
            </a>
        </td>
                <td class="value">42</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('C',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-C;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=C,-float%2C%20default%3D1.0">
                C
                <span class="param-doc-description"
                style="position-anchor: --doc-link-C;">
                C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive. The penalty<br>is a squared l2 penalty. For an intuitive visualization of the effects<br>of scaling the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>
            </a>
        </td>
                <td class="value">1.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('kernel',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-kernel;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=kernel,-%7B%27linear%27%2C%20%27poly%27%2C%20%27rbf%27%2C%20%27sigmoid%27%2C%20%27precomputed%27%7D%20or%20callable%2C%20%20%20%20%20%20%20%20%20%20default%3D%27rbf%27">
                kernel
                <span class="param-doc-description"
                style="position-anchor: --doc-link-kernel;">
                kernel: {&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;, &#x27;precomputed&#x27;} or callable,          default=&#x27;rbf&#x27;<br><br>Specifies the kernel type to be used in the algorithm. If<br>none is given, &#x27;rbf&#x27; will be used. If a callable is given it is used to<br>pre-compute the kernel matrix from data matrices; that matrix should be<br>an array of shape ``(n_samples, n_samples)``. For an intuitive<br>visualization of different kernel types see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.</span>
            </a>
        </td>
                <td class="value">&#x27;rbf&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('degree',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-degree;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=degree,-int%2C%20default%3D3">
                degree
                <span class="param-doc-description"
                style="position-anchor: --doc-link-degree;">
                degree: int, default=3<br><br>Degree of the polynomial kernel function (&#x27;poly&#x27;).<br>Must be non-negative. Ignored by all other kernels.</span>
            </a>
        </td>
                <td class="value">3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('gamma',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-gamma;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=gamma,-%7B%27scale%27%2C%20%27auto%27%7D%20or%20float%2C%20default%3D%27scale%27">
                gamma
                <span class="param-doc-description"
                style="position-anchor: --doc-link-gamma;">
                gamma: {&#x27;scale&#x27;, &#x27;auto&#x27;} or float, default=&#x27;scale&#x27;<br><br>Kernel coefficient for &#x27;rbf&#x27;, &#x27;poly&#x27; and &#x27;sigmoid&#x27;.<br><br>- if ``gamma=&#x27;scale&#x27;`` (default) is passed then it uses<br>  1 / (n_features * X.var()) as value of gamma,<br>- if &#x27;auto&#x27;, uses 1 / n_features<br>- if float, must be non-negative.<br><br>.. versionchanged:: 0.22<br>   The default value of ``gamma`` changed from &#x27;auto&#x27; to &#x27;scale&#x27;.</span>
            </a>
        </td>
                <td class="value">&#x27;scale&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('coef0',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-coef0;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=coef0,-float%2C%20default%3D0.0">
                coef0
                <span class="param-doc-description"
                style="position-anchor: --doc-link-coef0;">
                coef0: float, default=0.0<br><br>Independent term in kernel function.<br>It is only significant in &#x27;poly&#x27; and &#x27;sigmoid&#x27;.</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('shrinking',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-shrinking;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=shrinking,-bool%2C%20default%3DTrue">
                shrinking
                <span class="param-doc-description"
                style="position-anchor: --doc-link-shrinking;">
                shrinking: bool, default=True<br><br>Whether to use the shrinking heuristic.<br>See the :ref:`User Guide &lt;shrinking_svm&gt;`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('probability',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-probability;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=probability,-bool%2C%20default%3DFalse">
                probability
                <span class="param-doc-description"
                style="position-anchor: --doc-link-probability;">
                probability: bool, default=False<br><br>Whether to enable probability estimates. This must be enabled prior<br>to calling `fit`, will slow down that method as it internally uses<br>5-fold cross-validation, and `predict_proba` may be inconsistent with<br>`predict`. Read more in the :ref:`User Guide &lt;scores_probabilities&gt;`.<br><br>..deprecated:: 1.9<br>  The `probability` parameter is deprecated and will be removed in 1.11.<br>  Use `CalibratedClassifierCV(SVC(), ensemble=False)` instead of<br>  `SVC(probability=True)`.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-tol;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=tol,-float%2C%20default%3D1e-3">
                tol
                <span class="param-doc-description"
                style="position-anchor: --doc-link-tol;">
                tol: float, default=1e-3<br><br>Tolerance for stopping criterion.</span>
            </a>
        </td>
                <td class="value">0.001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('cache_size',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-cache_size;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=cache_size,-float%2C%20default%3D200">
                cache_size
                <span class="param-doc-description"
                style="position-anchor: --doc-link-cache_size;">
                cache_size: float, default=200<br><br>Specify the size of the kernel cache (in MB).</span>
            </a>
        </td>
                <td class="value">200</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('class_weight',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-class_weight;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
                class_weight
                <span class="param-doc-description"
                style="position-anchor: --doc-link-class_weight;">
                class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to class_weight[i]*C for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-verbose;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description"
                style="position-anchor: --doc-link-verbose;">
                verbose: bool, default=False<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in libsvm that, if enabled, may not work<br>properly in a multithreaded context.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-max_iter;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=max_iter,-int%2C%20default%3D-1">
                max_iter
                <span class="param-doc-description"
                style="position-anchor: --doc-link-max_iter;">
                max_iter: int, default=-1<br><br>Hard limit on iterations within solver, or -1 for no limit.</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('decision_function_shape',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-decision_function_shape;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=decision_function_shape,-%7B%27ovo%27%2C%20%27ovr%27%7D%2C%20default%3D%27ovr%27">
                decision_function_shape
                <span class="param-doc-description"
                style="position-anchor: --doc-link-decision_function_shape;">
                decision_function_shape: {&#x27;ovo&#x27;, &#x27;ovr&#x27;}, default=&#x27;ovr&#x27;<br><br>Whether to return a one-vs-rest (&#x27;ovr&#x27;) decision function of shape<br>(n_samples, n_classes) as all other classifiers, or the original<br>one-vs-one (&#x27;ovo&#x27;) decision function of libsvm which has shape<br>(n_samples, n_classes * (n_classes - 1) / 2). However, note that<br>internally, one-vs-one (&#x27;ovo&#x27;) is always used as a multi-class strategy<br>to train models; an ovr matrix is only constructed from the ovo matrix.<br>The parameter is ignored for binary classification.<br><br>.. versionchanged:: 0.19<br>    decision_function_shape is &#x27;ovr&#x27; by default.<br><br>.. versionadded:: 0.17<br>   *decision_function_shape=&#x27;ovr&#x27;* is recommended.<br><br>.. versionchanged:: 0.17<br>   Deprecated *decision_function_shape=&#x27;ovo&#x27; and None*.</span>
            </a>
        </td>
                <td class="value">&#x27;ovr&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('break_ties',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-break_ties;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=break_ties,-bool%2C%20default%3DFalse">
                break_ties
                <span class="param-doc-description"
                style="position-anchor: --doc-link-break_ties;">
                break_ties: bool, default=False<br><br>If true, ``decision_function_shape=&#x27;ovr&#x27;``, and number of classes &gt; 2,<br>:term:`predict` will break ties according to the confidence values of<br>:term:`decision_function`; otherwise the first class among the tied<br>classes is returned. Please note that breaking ties comes at a<br>relatively high computational cost compared to a simple predict. See<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` for an<br>example of its usage with ``decision_function_shape=&#x27;ovr&#x27;``.<br><br>.. versionadded:: 0.22</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
    
            <div class="estimator-table">
                <details>
                    <summary>Fitted attributes</summary>
                    <table class="parameters-table">
                        <tbody>
                            <tr>
                            <th>Name</th>
                            <th>Type</th>
                            <th>Value</th>
                            </tr>
                        
           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-class_weight_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=class_weight_,-ndarray%20of%20shape%20%28n_classes%2C%29">
                class_weight_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-class_weight_;">
                class_weight_: ndarray of shape (n_classes,)<br><br>Multipliers of parameter C for each class.<br>Computed based on the ``class_weight`` parameter.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[float64](2,)</td>
               <td>[1.,1.]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-classes_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29">
                classes_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-classes_;">
                classes_: ndarray of shape (n_classes,)<br><br>The classes labels.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[bool](2,)</td>
               <td>[False, True]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-dual_coef_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=dual_coef_,-ndarray%20or%20sparse%20array/matrix%20of%20shape%20%28n_classes%20-1%2C%20n_SV%29">
                dual_coef_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-dual_coef_;">
                dual_coef_: ndarray or sparse array/matrix of shape (n_classes -1, n_SV)<br><br>Dual coefficients of the support vector in the decision<br>function (see :ref:`sgd_mathematical_formulation`), multiplied by<br>their targets.<br>For multiclass, coefficient for all 1-vs-1 classifiers.<br>The layout of the coefficients in the multiclass case is somewhat<br>non-trivial. See the :ref:`multi-class section of the User Guide<br>&lt;svm_multi_class&gt;` for details.<br>If `X` is sparse, these will also be sparse.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[float64](1, 73)</td>
               <td>[[-1.,-1.,-1.,..., 1., 1., 1.]]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-fit_status_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=fit_status_,-int">
                fit_status_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-fit_status_;">
                fit_status_: int<br><br>0 if correctly fitted, 1 otherwise (will raise warning)</span>
            </a>
        </td>
               <td class="fitted-att-type">int</td>
               <td>0</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-intercept_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%28n_classes%20%2A%20%28n_classes%20-%201%29%20/%202%2C%29">
                intercept_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-intercept_;">
                intercept_: ndarray of shape (n_classes * (n_classes - 1) / 2,)<br><br>Constants in decision function.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[float64](1,)</td>
               <td>[-1.1]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-n_features_in_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=n_features_in_,-int">
                n_features_in_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-n_features_in_;">
                n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
               <td class="fitted-att-type">int</td>
               <td>13</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-n_iter_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=n_iter_,-ndarray%20of%20shape%20%28n_classes%20%2A%20%28n_classes%20-%201%29%20//%202%2C%29">
                n_iter_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-n_iter_;">
                n_iter_: ndarray of shape (n_classes * (n_classes - 1) // 2,)<br><br>Number of iterations run by the optimization routine to fit the model.<br>The shape of this attribute depends on the number of models optimized<br>which in turn depends on the number of classes.<br><br>.. versionadded:: 1.1</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[int32](1,)</td>
               <td>[45]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-n_support_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=n_support_,-ndarray%20of%20shape%20%28n_classes%2C%29%2C%20dtype%3Dint32">
                n_support_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-n_support_;">
                n_support_: ndarray of shape (n_classes,), dtype=int32<br><br>Number of support vectors for each class.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[int32](2,)</td>
               <td>[37,36]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-probA_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=probA_,-ndarray%20of%20shape%20%28n_classes%20%2A%20%28n_classes%20-%201%29%20/%202%29">
                probA_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-probA_;">
                probA_: ndarray of shape (n_classes * (n_classes - 1) / 2)<br><br>If `probability=True`, it corresponds to the parameters learned in<br>Platt scaling to produce probability estimates from decision values.<br>If `probability=False`, it&#x27;s an empty array. Platt scaling uses the<br>logistic function</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[float64](0,)</td>
               <td>[]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-probB_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=probB_,-ndarray%20of%20shape%20%28n_classes%20%2A%20%28n_classes%20-%201%29%20/%202%29">
                probB_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-probB_;">
                probB_: ndarray of shape (n_classes * (n_classes - 1) / 2)<br><br>If `probability=True`, it corresponds to the parameters learned in<br>Platt scaling. Platt scaling uses the logistic function<br>``1 / (1 + exp(decision_value * probA_ + probB_))``<br>where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For<br>more information on the multiclass case and training procedure see<br>section 8 of [1]_.<br><br>.. deprecated:: 1.9<br>    The attributes `probA_` and `probB_` are deprecated in version 1.9 and will<br>    be removed in 1.11.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[float64](0,)</td>
               <td>[]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-shape_fit_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=shape_fit_,-tuple%20of%20int%20of%20shape%20%28n_dimensions_of_X%2C%29">
                shape_fit_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-shape_fit_;">
                shape_fit_: tuple of int of shape (n_dimensions_of_X,)<br><br>Array dimensions of training vector ``X``.</span>
            </a>
        </td>
               <td class="fitted-att-type">tuple</td>
               <td>(133, 13)</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-support_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=support_,-ndarray%20of%20shape%20%28n_SV%29">
                support_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-support_;">
                support_: ndarray of shape (n_SV)<br><br>Indices of support vectors.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[int32](73,)</td>
               <td>[  1, 10, 11,...,118,119,126]</td>


           </tr>
    

           <tr class="default">
               <td class="param">
            <a class="param-doc-link"
                style="anchor-name: --doc-link-support_vectors_;"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.svm.SVC.html#:~:text=support_vectors_,-ndarray%20or%20sparse%20array/matrix%20of%20shape%20%28n_SV%2C%20n_features%29">
                support_vectors_
                <span class="param-doc-description"
                style="position-anchor: --doc-link-support_vectors_;">
                support_vectors_: ndarray or sparse array/matrix of shape (n_SV, n_features)<br><br>Support vectors. An empty array if kernel is precomputed.<br>If `X` is sparse, these will also be sparse.</span>
            </a>
        </td>
               <td class="fitted-att-type">ndarray[float64](73, 13)</td>
               <td>[[ 12.08,  2.08,  1.7 ,...,  1.27,  2.96,710.  ],
     [ 12.51,  1.73,  1.98,...,  1.04,  3.57,672.  ],
     [ 12.33,  0.99,  1.95,...,  1.06,  2.31,750.  ],
     ...,
     [ 12.86,  1.35,  2.32,...,  0.76,  1.29,630.  ],
     [ 12.79,  2.67,  2.48,...,  0.48,  1.47,480.  ],
     [ 14.13,  4.1 ,  2.74,...,  0.61,  1.6 ,560.  ]]</td>


           </tr>
    
                        </tbody>
                    </table>
                </details>
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.. GENERATED FROM PYTHON SOURCE LINES 36-42

Plotting the ROC Curve
----------------------
Next, we plot the ROC curve with a single call to
:func:`sklearn.metrics.RocCurveDisplay.from_estimator`. The returned
`svc_disp` object allows us to continue using the already computed ROC curve
for the SVC in future plots.

.. GENERATED FROM PYTHON SOURCE LINES 42-45

.. code-block:: Python

    svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
    plt.show()




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png
   :alt: plot roc curve visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 46-54

Training a Random Forest and Plotting the ROC Curve
---------------------------------------------------
We train a random forest classifier and create a plot comparing it to the SVC
ROC curve. Notice how `svc_disp` uses
:func:`~sklearn.metrics.RocCurveDisplay.plot` to plot the SVC ROC curve
without recomputing the values of the roc curve itself. Furthermore, we
pass `alpha=0.8` to the plot functions to adjust the alpha values of the
curves.

.. GENERATED FROM PYTHON SOURCE LINES 54-62

.. code-block:: Python

    rfc = RandomForestClassifier(n_estimators=10, random_state=42)
    rfc.fit(X_train, y_train)
    ax = plt.gca()
    rfc_disp = RocCurveDisplay.from_estimator(
        rfc, X_test, y_test, ax=ax, curve_kwargs=dict(alpha=0.8)
    )
    svc_disp.plot(ax=ax, curve_kwargs=dict(alpha=0.8))
    plt.show()



.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png
   :alt: plot roc curve visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.198 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py:

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.. include:: plot_roc_curve_visualization_api.recommendations


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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
