.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_ensemble_plot_forest_importances.py>`     to download the full example code or to run this example in your browser via Binder
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_ensemble_plot_forest_importances.py:


=========================================
Feature importances with forests of trees
=========================================

This examples shows the use of forests of trees to evaluate the importance of
features on an artificial classification task. The red bars are
the impurity-based feature importances of the forest,
along with their inter-trees variability.

As expected, the plot suggests that 3 features are informative, while the
remaining are not.

Warning: impurity-based feature importances can be misleading for high
cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.



.. image:: /auto_examples/ensemble/images/sphx_glr_plot_forest_importances_001.png
    :alt: Feature importances
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    Feature ranking:
    1. feature 1 (0.295902)
    2. feature 2 (0.208351)
    3. feature 0 (0.177632)
    4. feature 3 (0.047121)
    5. feature 6 (0.046303)
    6. feature 8 (0.046013)
    7. feature 7 (0.045575)
    8. feature 4 (0.044614)
    9. feature 9 (0.044577)
    10. feature 5 (0.043912)






|


.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.datasets import make_classification
    from sklearn.ensemble import ExtraTreesClassifier

    # Build a classification task using 3 informative features
    X, y = make_classification(n_samples=1000,
                               n_features=10,
                               n_informative=3,
                               n_redundant=0,
                               n_repeated=0,
                               n_classes=2,
                               random_state=0,
                               shuffle=False)

    # Build a forest and compute the impurity-based feature importances
    forest = ExtraTreesClassifier(n_estimators=250,
                                  random_state=0)

    forest.fit(X, y)
    importances = forest.feature_importances_
    std = np.std([tree.feature_importances_ for tree in forest.estimators_],
                 axis=0)
    indices = np.argsort(importances)[::-1]

    # Print the feature ranking
    print("Feature ranking:")

    for f in range(X.shape[1]):
        print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))

    # Plot the impurity-based feature importances of the forest
    plt.figure()
    plt.title("Feature importances")
    plt.bar(range(X.shape[1]), importances[indices],
            color="r", yerr=std[indices], align="center")
    plt.xticks(range(X.shape[1]), indices)
    plt.xlim([-1, X.shape[1]])
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.414 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_forest_importances.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_forest_importances.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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