Pushing the docs to dev/ for branch: master, commit 0c84afa5446cd606914a06089950014983d48a57

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -100,18 +100,23 @@
 # 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)
 
 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()

dev/_downloads/7ee55c12f8d3eb1dd8d2005d9dd7b6f1/plot_release_highlights_0_22_0.py

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.inspection import permutation_importance\n\nX, y = make_classification(random_state=0, n_features=5, n_informative=3)\nrf = RandomForestClassifier(random_state=0).fit(X, y)\nresult = permutation_importance(rf, X, y, n_repeats=10, random_state=0,\n                                n_jobs=-1)\n\nfig, ax = plt.subplots()\nsorted_idx = result.importances_mean.argsort()\nax.boxplot(result.importances[sorted_idx].T,\n           vert=False, labels=range(X.shape[1]))\nax.set_title(\"Permutation Importance of each feature\")\nax.set_ylabel(\"Features\")\nfig.tight_layout()\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.inspection import permutation_importance\n\nX, y = make_classification(random_state=0, n_features=5, n_informative=3)\nfeature_names = np.array([f'x_{i}' for i in range(X.shape[1])])\n\nrf = RandomForestClassifier(random_state=0).fit(X, y)\nresult = permutation_importance(rf, X, y, n_repeats=10, random_state=0,\n                                n_jobs=-1)\n\nfig, ax = plt.subplots()\nsorted_idx = result.importances_mean.argsort()\nax.boxplot(result.importances[sorted_idx].T,\n           vert=False, labels=feature_names[sorted_idx])\nax.set_title(\"Permutation Importance of each feature\")\nax.set_ylabel(\"Features\")\nfig.tight_layout()\nplt.show()"
       ]
     },
     {