sklearn.metrics.PrecisionRecallDisplay

class sklearn.metrics.PrecisionRecallDisplay(precision, recall, *, average_precision=None, estimator_name=None)[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, default=None

Average precision. If None, the average precision is not shown.

estimator_namestr, default=None

Name of estimator. If None, then the estimator name is not shown.

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=None, estimator_name=None)[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.

Examples using sklearn.metrics.PrecisionRecallDisplay