DOC Fix pos_label docstring in Display classes (scikit-learn#31696)

Author: lucyleeow

sklearn/calibration.py

@@ -1102,9 +1102,8 @@ class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
         Name of estimator. If None, the estimator name is not shown.
 
     pos_label : int, float, bool or str, default=None
-        The positive class when computing the calibration curve.
-        By default, `pos_label` is set to `estimators.classes_[1]` when using
-        `from_estimator` and set to 1 when using `from_predictions`.
+        The positive class when calibration curve computed.
+        If not `None`, this value is displayed in the x- and y-axes labels.
 
         .. versionadded:: 1.1
 
@@ -1385,7 +1384,8 @@ def from_predictions(
 
         pos_label : int, float, bool or str, default=None
             The positive class when computing the calibration curve.
-            By default `pos_label` is set to 1.
+            When `pos_label=None`, if `y_true` is in {-1, 1} or {0, 1},
+            `pos_label` is set to 1, otherwise an error will be raised.
 
             .. versionadded:: 1.1
 

sklearn/metrics/_plot/det_curve.py

@@ -34,7 +34,8 @@ class DetCurveDisplay(_BinaryClassifierCurveDisplayMixin):
         Name of estimator. If None, the estimator name is not shown.
 
     pos_label : int, float, bool or str, default=None
-        The label of the positive class.
+        The label of the positive class. If not `None`, this value is displayed in
+        the x- and y-axes labels.
 
     Attributes
     ----------
@@ -136,9 +137,8 @@ def from_estimator(
             exist :term:`decision_function` is tried next.
 
         pos_label : int, float, bool or str, default=None
-            The label of the positive class. When `pos_label=None`, if `y_true`
-            is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
-            error will be raised.
+            The label of the positive class. By default, `estimators.classes_[1]`
+            is considered as the positive class.
 
         name : str, default=None
             Name of DET curve for labeling. If `None`, use the name of the

sklearn/metrics/_plot/precision_recall_curve.py

@@ -40,8 +40,8 @@ class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin):
         Name of estimator. If None, then the estimator name is not shown.
 
     pos_label : int, float, bool or str, default=None
-        The class considered as the positive class. If None, the class will not
-        be shown in the legend.
+        The class considered the positive class when precision and recall metrics
+        computed. If not `None`, this value is displayed in the x- and y-axes labels.
 
         .. versionadded:: 0.24
 
@@ -449,7 +449,9 @@ def from_predictions(
 
         pos_label : int, float, bool or str, default=None
             The class considered as the positive class when computing the
-            precision and recall metrics.
+            precision and recall metrics. When `pos_label=None`, if `y_true` is
+            in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an error
+            will be raised.
 
         name : str, default=None
             Name for labeling curve. If `None`, name will be set to

sklearn/metrics/_plot/roc_curve.py

@@ -71,9 +71,8 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
         .. versionadded:: 1.7
 
     pos_label : int, float, bool or str, default=None
-        The class considered as the positive class when computing the roc auc
-        metrics. By default, `estimators.classes_[1]` is considered
-        as the positive class.
+        The class considered the positive class when ROC AUC metrics computed.
+        If not `None`, this value is displayed in the x- and y-axes labels.
 
         .. versionadded:: 0.24