@@ -83,7 +83,7 @@ def report_metrics(
8383 same parameter name with different values), you can use scikit-learn scorers
8484 as provided by :func:`sklearn.metrics.make_scorer`.
8585
86- pos_label : int, default=None
86+ pos_label : int, float, bool or str, default=None
8787 The positive class.
8888
8989 scoring_kwargs : dict, default=None
@@ -357,7 +357,7 @@ def precision(
357357 only the statistics of the positive class (i.e. equivalent to
358358 `average="binary"`).
359359
360- pos_label : int, default=None
360+ pos_label : int, float, bool or str, default=None
361361 The positive class.
362362
363363 Returns
@@ -434,7 +434,7 @@ def recall(
434434 only the statistics of the positive class (i.e. equivalent to
435435 `average="binary"`).
436436
437- pos_label : int, default=None
437+ pos_label : int, float, bool or str, default=None
438438 The positive class.
439439
440440 Returns
@@ -938,7 +938,7 @@ def roc(self, *, data_source="test", X=None, y=None, pos_label=None, ax=None):
938938 New target on which to compute the metric. By default, we use the target
939939 provided when creating the reporter.
940940
941- pos_label : str, default=None
941+ pos_label : int, float, bool or str, default=None
942942 The positive class.
943943
944944 ax : matplotlib.axes.Axes, default=None
@@ -995,7 +995,7 @@ def precision_recall(
995995 New target on which to compute the metric. By default, we use the target
996996 provided when creating the reporter.
997997
998- pos_label : str, default=None
998+ pos_label : int, float, bool or str, default=None
999999 The positive class.
10001000
10011001 ax : matplotlib.axes.Axes, default=None
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