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diff --git a/‎dev/_sources/auto_examples/applications/plot_outlier_detection_housing.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_stock_market.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt
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@@ -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>`.
 
@@ -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"
       ]
     },
     {
Original file line number	Diff line number	Diff line change
`@@ -184,7 +184,7 @@`
`184`	`184`	`"cell_type": "markdown",`
`185`	`185`	`"metadata": {},`
`186`	`186`	`"source": [`
`187`		- "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"
	`187`	+ "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"
`188`	`188`	`]`
`189`	`189`	`},`
`190`	`190`	`{`