Pushing the docs to dev/ for branch: master, commit a83b8e0d486acb6d0958602dc3b8b48de151e44a

Author: sklearn-ci

dev/_downloads/0e54710c34c6326f0886857069d1cc1f/plot_permutation_importance_multicollinear.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "result = permutation_importance(clf, X_train, y_train, n_repeats=10,\n                                random_state=42)\nperm_sorted_idx = result.importances_mean.argsort()\n\ntree_importance_sorted_idx = np.argsort(clf.feature_importances_)\ntree_indices = np.arange(0, len(clf.feature_importances_)) + 0.5\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))\nax1.barh(tree_indices,\n         clf.feature_importances_[tree_importance_sorted_idx], height=0.7)\nax1.set_yticklabels(data.feature_names)\nax1.set_yticks(tree_indices)\nax1.set_ylim((0, len(clf.feature_importances_)))\nax2.boxplot(result.importances[perm_sorted_idx].T, vert=False,\n            labels=data.feature_names)\nfig.tight_layout()\nplt.show()"
+        "result = permutation_importance(clf, X_train, y_train, n_repeats=10,\n                                random_state=42)\nperm_sorted_idx = result.importances_mean.argsort()\n\ntree_importance_sorted_idx = np.argsort(clf.feature_importances_)\ntree_indices = np.arange(0, len(clf.feature_importances_)) + 0.5\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))\nax1.barh(tree_indices,\n         clf.feature_importances_[tree_importance_sorted_idx], height=0.7)\nax1.set_yticklabels(data.feature_names[tree_importance_sorted_idx])\nax1.set_yticks(tree_indices)\nax1.set_ylim((0, len(clf.feature_importances_)))\nax2.boxplot(result.importances[perm_sorted_idx].T, vert=False,\n            labels=data.feature_names[perm_sorted_idx])\nfig.tight_layout()\nplt.show()"
       ]
     },
     {

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -60,11 +60,11 @@
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))
 ax1.barh(tree_indices,
          clf.feature_importances_[tree_importance_sorted_idx], height=0.7)
-ax1.set_yticklabels(data.feature_names)
+ax1.set_yticklabels(data.feature_names[tree_importance_sorted_idx])
 ax1.set_yticks(tree_indices)
 ax1.set_ylim((0, len(clf.feature_importances_)))
 ax2.boxplot(result.importances[perm_sorted_idx].T, vert=False,
-            labels=data.feature_names)
+            labels=data.feature_names[perm_sorted_idx])
 fig.tight_layout()
 plt.show()