Pushing the docs to dev/ for branch: master, commit fc46a13d57be800da2a8a6b2f8e2621d132ac508

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -246,11 +246,10 @@ def test_sklearn_compatible_estimator(estimator, check):
 # classification. Two averaging strategies are currently supported: the
 # one-vs-one algorithm computes the average of the pairwise ROC AUC scores, and
 # the one-vs-rest algorithm computes the average of the ROC AUC scores for each
-# class against all other classes. In both cases, the predicted labels are
-# provided in an array with values from 0 to ``n_classes``, and the scores
-# correspond to the probability estimates that a sample belongs to a particular
-# class. The OvO and OvR algorithms supports weighting uniformly
-# (``average='macro'``) and weighting by the prevalence
+# class against all other classes. In both cases, the multiclass ROC AUC scores
+# are computed from the probability estimates that a sample belongs to a
+# particular class according to the model. The OvO and OvR algorithms support
+# weighting uniformly (``average='macro'``) and weighting by the prevalence
 # (``average='weighted'``).
 #
 # Read more in the :ref:`User Guide <roc_metrics>`.

dev/_downloads/7ee55c12f8d3eb1dd8d2005d9dd7b6f1/plot_release_highlights_0_22_0.py

@@ -184,7 +184,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "ROC AUC now supports multiclass classification\n----------------------------------------------\nThe :func:`roc_auc_score` function can also be used in multi-class\nclassification. Two averaging strategies are currently supported: the\none-vs-one algorithm computes the average of the pairwise ROC AUC scores, and\nthe one-vs-rest algorithm computes the average of the ROC AUC scores for each\nclass against all other classes. In both cases, the predicted labels are\nprovided in an array with values from 0 to ``n_classes``, and the scores\ncorrespond to the probability estimates that a sample belongs to a particular\nclass. The OvO and OvR algorithms supports weighting uniformly\n(``average='macro'``) and weighting by the prevalence\n(``average='weighted'``).\n\nRead more in the `User Guide <roc_metrics>`.\n\n"
+        "ROC AUC now supports multiclass classification\n----------------------------------------------\nThe :func:`roc_auc_score` function can also be used in multi-class\nclassification. Two averaging strategies are currently supported: the\none-vs-one algorithm computes the average of the pairwise ROC AUC scores, and\nthe one-vs-rest algorithm computes the average of the ROC AUC scores for each\nclass against all other classes. In both cases, the multiclass ROC AUC scores\nare computed from the probability estimates that a sample belongs to a\nparticular class according to the model. The OvO and OvR algorithms support\nweighting uniformly (``average='macro'``) and weighting by the prevalence\n(``average='weighted'``).\n\nRead more in the `User Guide <roc_metrics>`.\n\n"
       ]
     },
     {