sklearn.metrics.DetCurveDisplay

class sklearn.metrics.DetCurveDisplay(*, fpr, fnr, estimator_name=None, pos_label=None) [source]

DET curve visualization.

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

Read more in the User Guide.

New in version 0.24.

Parameters

fprndarray

False positive rate.

tprndarray

True positive rate.

estimator_namestr, default=None

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

pos_labelstr or int, default=None

The label of the positive class.

Attributes

line_matplotlib Artist

DET Curve.

ax_matplotlib Axes

Axes with DET Curve.

figure_matplotlib Figure

Figure containing the curve.

See also

det_curve

Compute error rates for different probability thresholds.

plot_det_curve

Plot detection error tradeoff (DET) 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, fnr, thresholds = metrics.det_curve(y, pred)
>>> display = metrics.DetCurveDisplay(
...     fpr=fpr, fnr=fnr, estimator_name='example estimator'
... )
>>> display.plot()  
>>> plt.show()      

Methods

plot([ax, name])

Plot visualization.

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

Plot visualization.

Parameters

axmatplotlib axes, default=None

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

namestr, default=None

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

Returns

displayDetCurveDisplay

Object that stores computed values.