Note
Click here to download the full example code
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.
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)
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 0.440 seconds)
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http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html