.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/miscellaneous/plot_display_object_visualization.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py: =================================== Visualizations with Display Objects =================================== .. currentmodule:: sklearn.metrics In this example, we will construct display objects, :class:`ConfusionMatrixDisplay`, :class:`RocCurveDisplay`, and :class:`PrecisionRecallDisplay` directly from their respective metrics. This is an alternative to using their corresponding plot functions when a model's predictions are already computed or expensive to compute. Note that this is advanced usage, and in general we recommend using their respective plot functions. .. GENERATED FROM PYTHON SOURCE LINES 19-26 Load Data and train model ------------------------- For this example, we load a blood transfusion service center data set from `OpenML `. This is a binary classification problem where the target is whether an individual donated blood. Then the data is split into a train and test dataset and a logistic regression is fitted with the train dataset. .. GENERATED FROM PYTHON SOURCE LINES 26-38 .. code-block:: Python from sklearn.datasets import fetch_openml from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler X, y = fetch_openml(data_id=1464, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y) clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0)) clf.fit(X_train, y_train) .. raw:: html
Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('logisticregression', LogisticRegression(random_state=0))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 39-44 Create :class:`ConfusionMatrixDisplay` ############################################################################# With the fitted model, we compute the predictions of the model on the test dataset. These predictions are used to compute the confusion matrix which is plotted with the :class:`ConfusionMatrixDisplay` .. GENERATED FROM PYTHON SOURCE LINES 44-52 .. code-block:: Python from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix y_pred = clf.predict(X_test) cm = confusion_matrix(y_test, y_pred) cm_display = ConfusionMatrixDisplay(cm).plot() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_001.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 53-58 Create :class:`RocCurveDisplay` ############################################################################# The roc curve requires either the probabilities or the non-thresholded decision values from the estimator. Since the logistic regression provides a decision function, we will use it to plot the roc curve: .. GENERATED FROM PYTHON SOURCE LINES 58-65 .. code-block:: Python from sklearn.metrics import RocCurveDisplay, roc_curve y_score = clf.decision_function(X_test) fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_[1]) roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_002.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 66-70 Create :class:`PrecisionRecallDisplay` ############################################################################# Similarly, the precision recall curve can be plotted using `y_score` from the prevision sections. .. GENERATED FROM PYTHON SOURCE LINES 70-75 .. code-block:: Python from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1]) pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_003.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 76-82 Combining the display objects into a single plot ############################################################################# The display objects store the computed values that were passed as arguments. This allows for the visualizations to be easliy combined using matplotlib's API. In the following example, we place the displays next to each other in a row. .. GENERATED FROM PYTHON SOURCE LINES 82-90 .. code-block:: Python import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) roc_display.plot(ax=ax1) pr_display.plot(ax=ax2) plt.show() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_004.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.717 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/miscellaneous/plot_display_object_visualization.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_display_object_visualization.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_display_object_visualization.py ` .. include:: plot_display_object_visualization.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_