.. _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 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.



.. image:: /auto_examples/ensemble/images/sphx_glr_plot_forest_importances_001.png
    :align: center


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

 Out::

      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:: python

    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 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 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()

**Total running time of the script:**
(0 minutes 1.204 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_forest_importances.ipynb <plot_forest_importances.ipynb>`