sklearn.metrics
.PrecisionRecallDisplay¶
-
class
sklearn.metrics.
PrecisionRecallDisplay
(precision, recall, average_precision, estimator_name)[source]¶ Precision Recall visualization.
It is recommend to use
plot_precision_recall_curve
to create a visualizer. All parameters are stored as attributes.Read more in the User Guide.
- Parameters
precision : ndarray
Precision values.
recall : ndarray
Recall values.
average_precision : float
Average precision.
estimator_name : str
Name of estimator.
Attributes
line_
(matplotlib Artist) Precision recall curve.
ax_
(matplotlib Axes) Axes with precision recall curve.
figure_
(matplotlib Figure) Figure containing the curve.
Methods
plot
([ax, name])Plot visualization.
-
__init__
(precision, recall, average_precision, estimator_name)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
plot
(ax=None, name=None, **kwargs)[source]¶ Plot visualization.
Extra keyword arguments will be passed to matplotlib’s
plot
.- Parameters
ax : Matplotlib Axes, default=None
Axes object to plot on. If
None
, a new figure and axes is created.name : str, default=None
Name of precision recall curve for labeling. If
None
, use the name of the estimator.**kwargs : dict
Keyword arguments to be passed to matplotlib’s
plot
.- Returns
display :
PrecisionRecallDisplay
Object that stores computed values.