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

precisionndarray

Precision values.

recallndarray

Recall values.

average_precisionfloat

Average precision.

estimator_namestr

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(self[, ax, name])

Plot visualization.

__init__(self, precision, recall, average_precision, estimator_name)[source]¶

Initialize self. See help(type(self)) for accurate signature.

plot(self, ax=None, name=None, **kwargs)[source]¶

Plot visualization.

Extra keyword arguments will be passed to matplotlib’s plot.

Parameters

axMatplotlib Axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

namestr, default=None

Name of precision recall curve for labeling. If None, use the name of the estimator.

**kwargsdict

Keyword arguments to be passed to matplotlib’s plot.

Returns

displayPrecisionRecallDisplay

Object that stores computed values.