.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/model_selection/plot_cv_predict.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_model_selection_plot_cv_predict.py>`
        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_model_selection_plot_cv_predict.py:


====================================
Plotting Cross-Validated Predictions
====================================

This example shows how to use
:func:`~sklearn.model_selection.cross_val_predict` together with
:class:`~sklearn.metrics.PredictionErrorDisplay` to visualize prediction
errors.

.. 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 16-18

We will load the diabetes dataset and create an instance of a linear
regression model.

.. GENERATED FROM PYTHON SOURCE LINES 18-24

.. code-block:: Python

    from sklearn.datasets import load_diabetes
    from sklearn.linear_model import LinearRegression

    X, y = load_diabetes(return_X_y=True)
    lr = LinearRegression()








.. GENERATED FROM PYTHON SOURCE LINES 25-28

:func:`~sklearn.model_selection.cross_val_predict` returns an array of the
same size of `y` where each entry is a prediction obtained by cross
validation.

.. GENERATED FROM PYTHON SOURCE LINES 28-32

.. code-block:: Python

    from sklearn.model_selection import cross_val_predict

    y_pred = cross_val_predict(lr, X, y, cv=10)








.. GENERATED FROM PYTHON SOURCE LINES 33-42

Since `cv=10`, it means that we trained 10 models and each model was
used to predict on one of the 10 folds. We can now use the
:class:`~sklearn.metrics.PredictionErrorDisplay` to visualize the
prediction errors.

On the left axis, we plot the observed values :math:`y` vs. the predicted
values :math:`\hat{y}` given by the models. On the right axis, we plot the
residuals (i.e. the difference between the observed values and the predicted
values) vs. the predicted values.

.. GENERATED FROM PYTHON SOURCE LINES 42-69

.. code-block:: Python

    import matplotlib.pyplot as plt

    from sklearn.metrics import PredictionErrorDisplay

    fig, axs = plt.subplots(ncols=2, figsize=(8, 4))
    PredictionErrorDisplay.from_predictions(
        y,
        y_pred=y_pred,
        kind="actual_vs_predicted",
        subsample=100,
        ax=axs[0],
        random_state=0,
    )
    axs[0].set_title("Actual vs. Predicted values")
    PredictionErrorDisplay.from_predictions(
        y,
        y_pred=y_pred,
        kind="residual_vs_predicted",
        subsample=100,
        ax=axs[1],
        random_state=0,
    )
    axs[1].set_title("Residuals vs. Predicted Values")
    fig.suptitle("Plotting cross-validated predictions")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_cv_predict_001.png
   :alt: Plotting cross-validated predictions, Actual vs. Predicted values, Residuals vs. Predicted Values
   :srcset: /auto_examples/model_selection/images/sphx_glr_plot_cv_predict_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 70-83

It is important to note that we used
:func:`~sklearn.model_selection.cross_val_predict` for visualization
purpose only in this example.

It would be problematic to
quantitatively assess the model performance by computing a single
performance metric from the concatenated predictions returned by
:func:`~sklearn.model_selection.cross_val_predict`
when the different CV folds vary by size and distributions.

It is recommended to compute per-fold performance metrics using:
:func:`~sklearn.model_selection.cross_val_score` or
:func:`~sklearn.model_selection.cross_validate` instead.


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

   **Total running time of the script:** (0 minutes 0.183 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_cv_predict.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_cv_predict.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/model_selection/plot_cv_predict.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_cv_predict.ipynb <plot_cv_predict.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_cv_predict.py <plot_cv_predict.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_cv_predict.zip <plot_cv_predict.zip>`


.. include:: plot_cv_predict.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

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