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diff --git a/‎examples/ensemble/plot_forest_importances.py
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+98-48Lines changed: 98 additions & 48 deletions b/‎examples/ensemble/plot_forest_importances.py
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+98-48Lines changed: 98 additions & 48 deletions
@@ -1,60 +1,110 @@
 """
-=========================================
-Feature importances with forests of trees
-=========================================
+==========================================
+Feature importances with a forest 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.
+This example shows the use of a forest of trees to evaluate the importance of
+features on an artificial classification task. The blue bars are the feature
+importances of the forest, along with their inter-trees variability represented
+by the error bars.
 
 As expected, the plot suggests that 3 features are informative, while the
 remaining are not.
-
-.. warning::
-    Impurity-based feature importances can be misleading for high cardinality
-    features (many unique values). See
-    :func:`sklearn.inspection.permutation_importance` as an alternative.
 """
 print(__doc__)
-
-import numpy as np
 import matplotlib.pyplot as plt
 
+# %%
+# Data generation and model fitting
+# ---------------------------------
+# We generate a synthetic dataset with only 3 informative features. We will
+# explicitly not shuffle the dataset to ensure that the informative features
+# will correspond to the three first columns of X. In addition, we will split
+# our dataset into training and testing subsets.
 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)
+from sklearn.model_selection import train_test_split
+
+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)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, stratify=y, random_state=42)
+
+# %%
+# A random forest classifier will be fitted to compute the feature importances.
+from sklearn.ensemble import RandomForestClassifier
+
+feature_names = [f'feature {i}' for i in range(X.shape[1])]
+forest = RandomForestClassifier(random_state=0)
+forest.fit(X_train, y_train)
+
+# %%
+# Feature importance based on mean decrease in impurity
+# -----------------------------------------------------
+# Feature importances are provided by the fitted attribute
+# `feature_importances_` and they are computed as the mean and standard
+# deviation of accumulation of the impurity decrease within each tree.
+#
+# .. warning::
+#     Impurity-based feature importances can be misleading for high cardinality
+#     features (many unique values). See :ref:`permutation_importance` as
+#     an alternative below.
+import time
+import numpy as np
+
+start_time = time.time()
 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 impurity-based 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]])
+std = np.std([
+    tree.feature_importances_ for tree in forest.estimators_], axis=0)
+elapsed_time = time.time() - start_time
+
+print(f"Elapsed time to compute the importances: "
+      f"{elapsed_time:.3f} seconds")
+
+# %%
+# Let's plot the impurity-based importance.
+import pandas as pd
+forest_importances = pd.Series(importances, index=feature_names)
+
+fig, ax = plt.subplots()
+forest_importances.plot.bar(yerr=std, ax=ax)
+ax.set_title("Feature importances using MDI")
+ax.set_ylabel("Mean decrease in impurity")
+fig.tight_layout()
+
+# %%
+# We observe that, as expected, the three first features are found important.
+#
+# Feature importance based on feature permutation
+# -----------------------------------------------
+# Permutation feature importance overcomes limitations of the impurity-based
+# feature importance: they do not have a bias toward high-cardinality features
+# and can be computed on a left-out test set.
+from sklearn.inspection import permutation_importance
+
+start_time = time.time()
+result = permutation_importance(
+    forest, X_test, y_test, n_repeats=10, random_state=42, n_jobs=2)
+elapsed_time = time.time() - start_time
+print(f"Elapsed time to compute the importances: "
+      f"{elapsed_time:.3f} seconds")
+
+forest_importances = pd.Series(result.importances_mean, index=feature_names)
+
+# %%
+# The computation for full permutation importance is more costly. Features are
+# shuffled n times and the model refitted to estimate the importance of it.
+# Please see :ref:`permutation_importance` for more details. We can now plot
+# the importance ranking.
+
+fig, ax = plt.subplots()
+forest_importances.plot.bar(yerr=result.importances_std, ax=ax)
+ax.set_title("Feature importances using permutation on full model")
+ax.set_ylabel("Mean accuracy decrease")
+fig.tight_layout()
 plt.show()
+
+# %%
+# The same features are detected as most important using both methods. Although
+# the relative importances vary. As seen on the plots, MDI is less likely than
+# permutation importance to fully omit a feature.