Pushing the docs to dev/ for branch: master, commit 2f6e22b39841678286c6cc9c1842e6c695ed10b6

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Feature importances with forests of trees\n\n\nThis examples shows the use of forests of trees to evaluate the importance of\nfeatures on an artificial classification task. The red bars are\nthe impurity-based feature importances of the forest,\nalong with their inter-trees variability.\n\nAs expected, the plot suggests that 3 features are informative, while the\nremaining are not.\n\nWarning: impurity-based feature importances can be misleading for high\ncardinality features (many unique values). See\n:func:`sklearn.inspection.permutation_importance` as an alternative.\n"
+        "\n# Feature importances with forests of trees\n\n\nThis examples shows the use of forests of trees to evaluate the importance of\nfeatures on an artificial classification task. The red bars are\nthe impurity-based feature importances of the forest,\nalong with their inter-trees variability.\n\nAs expected, the plot suggests that 3 features are informative, while the\nremaining are not.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Impurity-based feature importances can be misleading for high cardinality\n    features (many unique values). See\n    :func:`sklearn.inspection.permutation_importance` as an alternative.</p></div>\n"
       ]
     },
     {

dev/_downloads/5e3b69cd18b43dd31a46a35ccba413e7/plot_forest_importances.ipynb

@@ -11,10 +11,10 @@
 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.
-
+.. 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__)