diff --git a/skore/src/skore/sklearn/_estimator/metrics_accessor.py b/skore/src/skore/sklearn/_estimator/metrics_accessor.py index 40869210b1..a464bfd4ff 100644 --- a/skore/src/skore/sklearn/_estimator/metrics_accessor.py +++ b/skore/src/skore/sklearn/_estimator/metrics_accessor.py @@ -83,7 +83,7 @@ def report_metrics( same parameter name with different values), you can use scikit-learn scorers as provided by :func:`sklearn.metrics.make_scorer`. - pos_label : int, default=None + pos_label : int, float, bool or str, default=None The positive class. scoring_kwargs : dict, default=None @@ -357,7 +357,7 @@ def precision( only the statistics of the positive class (i.e. equivalent to `average="binary"`). - pos_label : int, default=None + pos_label : int, float, bool or str, default=None The positive class. Returns @@ -434,7 +434,7 @@ def recall( only the statistics of the positive class (i.e. equivalent to `average="binary"`). - pos_label : int, default=None + pos_label : int, float, bool or str, default=None The positive class. Returns @@ -938,7 +938,7 @@ def roc(self, *, data_source="test", X=None, y=None, pos_label=None, ax=None): New target on which to compute the metric. By default, we use the target provided when creating the reporter. - pos_label : str, default=None + pos_label : int, float, bool or str, default=None The positive class. ax : matplotlib.axes.Axes, default=None @@ -995,7 +995,7 @@ def precision_recall( New target on which to compute the metric. By default, we use the target provided when creating the reporter. - pos_label : str, default=None + pos_label : int, float, bool or str, default=None The positive class. ax : matplotlib.axes.Axes, default=None diff --git a/skore/src/skore/sklearn/_estimator/report.py b/skore/src/skore/sklearn/_estimator/report.py index a99fa7cb4d..bd9dbe6d19 100644 --- a/skore/src/skore/sklearn/_estimator/report.py +++ b/skore/src/skore/sklearn/_estimator/report.py @@ -288,7 +288,7 @@ def _get_cached_response_values( response_method : str The response method. - pos_label : str, default=None + pos_label : int, float, bool or str, default=None The positive label. data_source : {"test", "train", "X_y"}, default="test" diff --git a/skore/src/skore/sklearn/_plot/roc_curve.py b/skore/src/skore/sklearn/_plot/roc_curve.py index 013d491e3d..a15780bc70 100644 --- a/skore/src/skore/sklearn/_plot/roc_curve.py +++ b/skore/src/skore/sklearn/_plot/roc_curve.py @@ -56,7 +56,7 @@ class RocCurveDisplay(HelpDisplayMixin, _ClassifierCurveDisplayMixin): estimator_name : str Name of the estimator. - pos_label : str, default=None + pos_label : int, float, bool or str, default=None The class considered as positive. Only meaningful for binary classification. data_source : {"train", "test", "X_y"}, default=None