diff --git a/examples/release_highlights/plot_release_highlights_0_22_0.py b/examples/release_highlights/plot_release_highlights_0_22_0.py
index 0d53dea9f7640..3e01cebbc60fd 100644
--- a/examples/release_highlights/plot_release_highlights_0_22_0.py
+++ b/examples/release_highlights/plot_release_highlights_0_22_0.py
@@ -100,10 +100,15 @@
 # The :func:`inspection.permutation_importance` can be used to get an
 # estimate of the importance of each feature, for any fitted estimator:
 
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_classification
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.inspection import permutation_importance
 
 X, y = make_classification(random_state=0, n_features=5, n_informative=3)
+feature_names = np.array([f'x_{i}' for i in range(X.shape[1])])
+
 rf = RandomForestClassifier(random_state=0).fit(X, y)
 result = permutation_importance(rf, X, y, n_repeats=10, random_state=0,
                                 n_jobs=-1)
@@ -111,7 +116,7 @@
 fig, ax = plt.subplots()
 sorted_idx = result.importances_mean.argsort()
 ax.boxplot(result.importances[sorted_idx].T,
-           vert=False, labels=range(X.shape[1]))
+           vert=False, labels=feature_names[sorted_idx])
 ax.set_title("Permutation Importance of each feature")
 ax.set_ylabel("Features")
 fig.tight_layout()