scikit-learn
diff --git a/‎sklearn/metrics/_plot/precision_recall_curve.py
Copy file name to clipboardExpand all lines: sklearn/metrics/_plot/precision_recall_curve.py
+14-1 b/‎sklearn/metrics/_plot/precision_recall_curve.py
Copy file name to clipboardExpand all lines: sklearn/metrics/_plot/precision_recall_curve.py
+14-1
@@ -20,7 +20,10 @@ class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin):
     a :class:`~sklearn.metrics.PrecisionRecallDisplay`. All parameters are
     stored as attributes.
 
-    Read more in the :ref:`User Guide <visualizations>`.
+    For general information regarding `scikit-learn` visualization tools, see
+    the :ref:`Visualization Guide <visualizations>`.
+    For guidance on interpreting these plots, refer to the :ref:`Model
+    Evaluation Guide <precision_recall_f_measure_metrics>`.
 
     Parameters
     ----------
@@ -276,6 +279,11 @@ def from_estimator(
     ):
         """Plot precision-recall curve given an estimator and some data.
 
+        For general information regarding `scikit-learn` visualization tools, see
+        the :ref:`Visualization Guide <visualizations>`.
+        For guidance on interpreting these plots, refer to the :ref:`Model
+        Evaluation Guide <precision_recall_f_measure_metrics>`.
+
         Parameters
         ----------
         estimator : estimator instance
@@ -416,6 +424,11 @@ def from_predictions(
     ):
         """Plot precision-recall curve given binary class predictions.
 
+        For general information regarding `scikit-learn` visualization tools, see
+        the :ref:`Visualization Guide <visualizations>`.
+        For guidance on interpreting these plots, refer to the :ref:`Model
+        Evaluation Guide <precision_recall_f_measure_metrics>`.
+
         Parameters
         ----------
         y_true : array-like of shape (n_samples,)