`sklearn.metrics`.RocCurveDisplay¶

class sklearn.metrics.RocCurveDisplay(fpr, tpr, roc_auc, estimator_name)[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: Area under ROC curve.
estimator_namestr: Name of estimator.

Attributes

line_matplotlib Artist: ROC Curve.
ax_matplotlib Axes: Axes with ROC Curve.
figure_matplotlib Figure: Figure containing the curve.

Examples

>>> import matplotlib.pyplot as plt  # doctest: +SKIP
>>> 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()  # doctest: +SKIP
>>> plt.show()      # doctest: +SKIP

Methods

plot(self[, ax, name])

Plot visualization

__init__(self, fpr, tpr, roc_auc, 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 ROC Curve for labeling. If None, use the name of the estimator.

Returns

displayRocCurveDisplay: Object that stores computed values.

sklearn.metrics.RocCurveDisplay¶

`sklearn.metrics`.RocCurveDisplay¶