sklearn.metrics.PrecisionRecallDisplay¶

class sklearn.metrics.PrecisionRecallDisplay(precision, recall, *, average_precision=None, estimator_name=None, pos_label=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.

pos_labelstr or int, default=None

The class considered as the positive class. If None, the class will not be shown in the legend.

New in version 0.24.

Attributes

line_matplotlib Artist

Precision recall curve.

ax_matplotlib Axes

Axes with precision recall curve.

figure_matplotlib Figure

Figure containing the curve.

See also

precision_recall_curve

Compute precision-recall pairs for different probability thresholds.

plot_precision_recall_curve

Plot Precision Recall Curve for binary classifiers.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import (precision_recall_curve,
...                              PrecisionRecallDisplay)
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
...                                                     random_state=0)
>>> clf = SVC(random_state=0)
>>> clf.fit(X_train, y_train)
SVC(random_state=0)
>>> predictions = clf.predict(X_test)
>>> precision, recall, _ = precision_recall_curve(y_test, predictions)
>>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
>>> disp.plot() 

Methods

plot([ax, name])

Plot visualization.

plot(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¶