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.
Impurity-based feature importances can be misleading for high cardinality
features (many unique values). See
sklearn.inspection.permutation_importance as an alternative.
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 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): 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), importances[indices], color="r", yerr=std[indices], align="center") plt.xticks(range(X.shape), indices) plt.xlim([-1, X.shape]) plt.show()
Total running time of the script: ( 0 minutes 0.378 seconds)