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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_packages_scikit-learn_auto_examples_plot_measuring_performance.py>`
        to download the full example code

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

.. _sphx_glr_packages_scikit-learn_auto_examples_plot_measuring_performance.py:


Measuring Decision Tree performance
====================================

Demonstrates overfit when testing on train set.

.. GENERATED FROM PYTHON SOURCE LINES 9-10

Get the data

.. GENERATED FROM PYTHON SOURCE LINES 10-14

.. code-block:: default


    from sklearn.datasets import fetch_california_housing
    data = fetch_california_housing(as_frame=True)








.. GENERATED FROM PYTHON SOURCE LINES 15-16

Train and test a model

.. GENERATED FROM PYTHON SOURCE LINES 16-22

.. code-block:: default

    from sklearn.tree import DecisionTreeRegressor
    clf = DecisionTreeRegressor().fit(data.data, data.target)

    predicted = clf.predict(data.data)
    expected = data.target








.. GENERATED FROM PYTHON SOURCE LINES 23-24

Plot predicted as a function of expected

.. GENERATED FROM PYTHON SOURCE LINES 24-34

.. code-block:: default


    import matplotlib.pyplot as plt
    plt.figure(figsize=(4, 3))
    plt.scatter(expected, predicted)
    plt.plot([0, 5], [0, 5], '--k')
    plt.axis('tight')
    plt.xlabel('True price ($100k)')
    plt.ylabel('Predicted price ($100k)')
    plt.tight_layout()




.. image-sg:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_measuring_performance_001.png
   :alt: plot measuring performance
   :srcset: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_measuring_performance_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 35-41

Pretty much no errors!

This is too good to be true: we are testing the model on the train
data, which is not a mesure of generalization.

**The results are not valid**


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

   **Total running time of the script:** ( 0 minutes  1.533 seconds)


.. _sphx_glr_download_packages_scikit-learn_auto_examples_plot_measuring_performance.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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