.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/stats/plot_residuals_distribution_script.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_stats_plot_residuals_distribution_script.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_stats_plot_residuals_distribution_script.py:


plot_residuals_distribution with examples
==========================================

An example showing the :py:func:`~scikitplot.api.metrics.plot_residuals_distribution` function
used by a scikit-learn regressor.

.. GENERATED FROM PYTHON SOURCE LINES 8-12

.. code-block:: Python
   :lineno-start: 9


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








.. GENERATED FROM PYTHON SOURCE LINES 13-15

Import scikit-plots
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 15-31

.. code-block:: Python
   :lineno-start: 16


    from sklearn.datasets import (
        load_diabetes as load_data,
    )
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import train_test_split

    import numpy as np

    np.random.seed(0)  # reproducibility
    # importing pylab or pyplot
    import matplotlib.pyplot as plt

    # Import scikit-plots
    import scikitplot as sp








.. GENERATED FROM PYTHON SOURCE LINES 32-34

Loading the dataset
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 34-39

.. code-block:: Python
   :lineno-start: 35


    # Load the data
    X, y = load_data(return_X_y=True, as_frame=True)
    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 40-42

Model Training
--------------

.. GENERATED FROM PYTHON SOURCE LINES 42-49

.. code-block:: Python
   :lineno-start: 43


    # Create an instance of the LogisticRegression
    model = LinearRegression().fit(X_train, y_train)

    # Perform predictions
    y_val_pred = model.predict(X_val)








.. GENERATED FROM PYTHON SOURCE LINES 50-52

Plot!
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 52-65

.. code-block:: Python
   :lineno-start: 53


    # Plot!
    ax = sp.metrics.plot_residuals_distribution(
        y_val,
        y_val_pred,
        dist_type="normal",
        save_fig=True,
        save_fig_filename="",
        # overwrite=True,
        add_timestamp=True,
        verbose=True,
    )




.. image-sg:: /auto_examples/stats/images/sphx_glr_plot_residuals_distribution_script_001.png
   :alt: Histogram of Residuals, Q-Q Plot: Fitted Normal μ=-4.45, σ=55.28, Q-Q Plot: Standard Normal mean=0, std=1
   :srcset: /auto_examples/stats/images/sphx_glr_plot_residuals_distribution_script_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Fitted mean-mu (μ): -4.4509
    Fitted std (σ)    : 55.2768
    [INFO] Saving path to: /home/circleci/repo/galleries/examples/stats/result_images/plot_residuals_distribution_20250629_134215Z.png
    [INFO] Plot saved to: /home/circleci/repo/galleries/examples/stats/result_images/plot_residuals_distribution_20250629_134215Z.png




.. GENERATED FROM PYTHON SOURCE LINES 66-83

.. admonition:: References

   The use of the following functions, methods, classes and modules is shown
   in this example:

   - https://www.itl.nist.gov/div898/handbook/pri/section2/pri24.htm
   - https://online.stat.psu.edu/stat462/node/122/

.. tags::

   model-type: regression
   model-workflow: model evaluation
   plot-type: histogram
   plot-type: qqplot
   domain: statistics
   level: intermediate
   purpose: showcase


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

   **Total running time of the script:** (0 minutes 0.722 seconds)


.. _sphx_glr_download_auto_examples_stats_plot_residuals_distribution_script.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-plots/scikit-plots/main?urlpath=lab/tree/notebooks/auto_examples/stats/plot_residuals_distribution_script.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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

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

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


.. include:: plot_residuals_distribution_script.recommendations


.. only:: html

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

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