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"""
=========================================
Feature importances with forests of trees
=========================================
This examples shows the use of forests of trees to evaluate the importance of
features on an artifical classification task. The red plots are the feature
importances of each individual tree, and the blue plot is the feature
importance of the whole forest.
As expected, the knee in the blue plot suggests that 3 features are
informative, while the remaining are not.
"""
print __doc__
import numpy as np
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,
compute_importances=True,
random_state=0)
forest.fit(X, y)
importances = forest.feature_importances_
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print "Feature ranking:"
for f in xrange(10):
print "%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]])
# Plot the feature importances of the trees and of the forest
import pylab as pl
pl.figure()
pl.title("Feature importances")
for tree in forest.estimators_:
pl.plot(xrange(10), tree.feature_importances_[indices], "r")
pl.plot(xrange(10), importances[indices], "b")
pl.show()
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