sklearn.metrics.RocCurveDisplay

class sklearn.metrics.RocCurveDisplay(*, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None) [source]

ROC Curve visualization.

It is recommend to use plot_roc_curve to create a visualizer. All parameters are stored as attributes.

Read more in the User Guide.

Parameters

fprndarray

False positive rate.

tprndarray

True positive rate.

roc_aucfloat, default=None

Area under ROC curve. If None, the roc_auc score is not shown.

estimator_namestr, default=None

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

pos_labelstr or int, default=None

The class considered as the positive class when computing the roc auc metrics. By default, estimators.classes_[1] is considered as the positive class.

New in version 0.24.

Attributes

line_matplotlib Artist

ROC Curve.

ax_matplotlib Axes

Axes with ROC Curve.

figure_matplotlib Figure

Figure containing the curve.

See also

roc_curve

Compute Receiver operating characteristic (ROC) curve.

plot_roc_curve

Plot Receiver operating characteristic (ROC) curve.

roc_auc_score

Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt  
>>> import numpy as np
>>> from sklearn import metrics
>>> y = np.array([0, 0, 1, 1])
>>> pred = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y, pred)
>>> roc_auc = metrics.auc(fpr, tpr)
>>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,                                          estimator_name='example estimator')
>>> display.plot()  
>>> plt.show()      

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 ROC Curve for labeling. If None, use the name of the estimator.

Returns

displayRocCurveDisplay

Object that stores computed values.

Examples using sklearn.metrics.RocCurveDisplay