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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_mpg.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_mpg.py:


======================================
Plotting Regression Forest Error Bars
======================================

This example demonstrates using `forestci` to calculate the error bars of
the predictions of a :class:`sklearn.ensemble.RandomForestRegressor` object.

The data used here are a classical machine learning data-set, describing
various features of different cars, and their MPG.

.. GENERATED FROM PYTHON SOURCE LINES 12-63



.. image-sg:: /auto_examples/images/sphx_glr_plot_mpg_001.png
   :alt: plot mpg
   :srcset: /auto_examples/images/sphx_glr_plot_mpg_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    # Regression Forest Example
    import numpy as np
    from matplotlib import pyplot as plt
    from sklearn.ensemble import RandomForestRegressor
    import sklearn.model_selection as xval
    from sklearn.datasets import fetch_openml
    import forestci as fci

    # retreive mpg data from machine learning library
    mpg_data = fetch_openml(data_id=196)

    # separate mpg data into predictors and outcome variable
    mpg_X = mpg_data["data"]
    mpg_y = mpg_data["target"]

    # remove rows where the data is nan
    not_null_sel = np.where(mpg_X.isna().sum(axis=1).values == 0)
    mpg_X = mpg_X.values[not_null_sel]
    mpg_y = mpg_y.values[not_null_sel]

    # split mpg data into training and test set
    mpg_X_train, mpg_X_test, mpg_y_train, mpg_y_test = xval.train_test_split(
        mpg_X,
        mpg_y,
        test_size=0.25,
        random_state=42)

    # Create RandomForestRegressor
    n_trees = 2000
    mpg_forest = RandomForestRegressor(n_estimators=n_trees, random_state=42)
    mpg_forest.fit(mpg_X_train, mpg_y_train)
    mpg_y_hat = mpg_forest.predict(mpg_X_test)

    # Plot predicted MPG without error bars
    plt.scatter(mpg_y_test, mpg_y_hat)
    plt.plot([5, 45], [5, 45], 'k--')
    plt.xlabel('Reported MPG')
    plt.ylabel('Predicted MPG')
    plt.show()

    # Calculate the variance
    mpg_V_IJ_unbiased = fci.random_forest_error(mpg_forest, mpg_X_train,
                                                mpg_X_test)

    # Plot error bars for predicted MPG using unbiased variance
    plt.errorbar(mpg_y_test, mpg_y_hat, yerr=np.sqrt(mpg_V_IJ_unbiased), fmt='o')
    plt.plot([5, 45], [5, 45], 'k--')
    plt.xlabel('Reported MPG')
    plt.ylabel('Predicted MPG')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  9.053 seconds)


.. _sphx_glr_download_auto_examples_plot_mpg.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



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

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



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

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


.. only:: html

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

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