61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""
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=========================================
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Feature importances with forests of trees
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=========================================
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This examples shows the use of forests of trees to evaluate the importance of
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features on an artificial classification task. The red bars are
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the impurity-based feature importances of the forest,
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along with their inter-trees variability.
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As expected, the plot suggests that 3 features are informative, while the
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remaining are not.
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.. warning::
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Impurity-based feature importances can be misleading for high cardinality
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features (many unique values). See
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:func:`sklearn.inspection.permutation_importance` as an alternative.
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"""
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_classification
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from sklearn.ensemble import ExtraTreesClassifier
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# Build a classification task using 3 informative features
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X, y = make_classification(n_samples=1000,
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n_features=10,
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n_informative=3,
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n_redundant=0,
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n_repeated=0,
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n_classes=2,
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random_state=0,
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shuffle=False)
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# Build a forest and compute the impurity-based feature importances
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forest = ExtraTreesClassifier(n_estimators=250,
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random_state=0)
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forest.fit(X, y)
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importances = forest.feature_importances_
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std = np.std([tree.feature_importances_ for tree in forest.estimators_],
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axis=0)
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indices = np.argsort(importances)[::-1]
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# Print the feature ranking
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print("Feature ranking:")
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for f in range(X.shape[1]):
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print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
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# Plot the impurity-based feature importances of the forest
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plt.figure()
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plt.title("Feature importances")
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plt.bar(range(X.shape[1]), importances[indices],
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color="r", yerr=std[indices], align="center")
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plt.xticks(range(X.shape[1]), indices)
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plt.xlim([-1, X.shape[1]])
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plt.show()
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