sklearn.metrics.RocCurveDisplay

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

ROC Curve visualization.

It is recommend to use from_estimator or from_predictions to create a RocCurveDisplay. 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_labelint, float, bool or str, 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.

chance_level_matplotlib Artist or None

The chance level line. It is None if the chance level is not plotted.

New in version 1.3.

ax_matplotlib Axes

Axes with ROC Curve.

figure_matplotlib Figure

Figure containing the curve.

See also

roc_curve

Compute Receiver operating characteristic (ROC) curve.

RocCurveDisplay.from_estimator

Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data.

RocCurveDisplay.from_predictions

Plot Receiver Operating Characteristic (ROC) curve given the true and predicted values.

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

from_estimator(estimator, X, y, *[, ...])	Create a ROC Curve display from an estimator.
from_predictions(y_true, y_pred, *[, ...])	Plot ROC curve given the true and predicted values.
plot([ax, name, plot_chance_level, ...])	Plot visualization.

classmethod from_estimator(estimator, X, y, *, sample_weight=None, drop_intermediate=True, response_method='auto', pos_label=None, name=None, ax=None, plot_chance_level=False, chance_level_kw=None, **kwargs)

Create a ROC Curve display from an estimator.

Parameters:

estimatorestimator instance

Fitted classifier or a fitted Pipeline in which the last estimator is a classifier.

X{array-like, sparse matrix} of shape (n_samples, n_features)

Input values.

yarray-like of shape (n_samples,)

Target values.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

drop_intermediatebool, default=True

Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

response_method{‘predict_proba’, ‘decision_function’, ‘auto’} default=’auto’

Specifies whether to use predict_proba or decision_function as the target response. If set to ‘auto’, predict_proba is tried first and if it does not exist decision_function is tried next.

pos_labelint, float, bool or str, 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.

namestr, default=None

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

axmatplotlib axes, default=None

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

plot_chance_levelbool, default=False

Whether to plot the chance level.

New in version 1.3.

chance_level_kwdict, default=None

Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

New in version 1.3.

**kwargsdict

Keyword arguments to be passed to matplotlib’s plot.

Returns:

displayRocCurveDisplay

The ROC Curve display.

See also

roc_curve

Compute Receiver operating characteristic (ROC) curve.

RocCurveDisplay.from_predictions

ROC Curve visualization given the probabilities of scores of a classifier.

roc_auc_score

Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> 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).fit(X_train, y_train)
>>> RocCurveDisplay.from_estimator(
...    clf, X_test, y_test)
<...>
>>> plt.show()

classmethod from_predictions(y_true, y_pred, *, sample_weight=None, drop_intermediate=True, pos_label=None, name=None, ax=None, plot_chance_level=False, chance_level_kw=None, **kwargs)

Plot ROC curve given the true and predicted values.

Read more in the User Guide.

New in version 1.0.

Parameters:

y_truearray-like of shape (n_samples,)

True labels.

y_predarray-like of shape (n_samples,)

Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

drop_intermediatebool, default=True

Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.

pos_labelint, float, bool or str, default=None

The label of the positive class. When pos_label=None, if y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise an error will be raised.

namestr, default=None

Name of ROC curve for labeling. If None, name will be set to "Classifier".

axmatplotlib axes, default=None

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

plot_chance_levelbool, default=False

Whether to plot the chance level.

New in version 1.3.

chance_level_kwdict, default=None

Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

New in version 1.3.

**kwargsdict

Additional keywords arguments passed to matplotlib plot function.

Returns:

displayRocCurveDisplay

Object that stores computed values.

See also

roc_curve

Compute Receiver operating characteristic (ROC) curve.

RocCurveDisplay.from_estimator

ROC Curve visualization given an estimator and some data.

roc_auc_score

Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> 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).fit(X_train, y_train)
>>> y_pred = clf.decision_function(X_test)
>>> RocCurveDisplay.from_predictions(
...    y_test, y_pred)
<...>
>>> plt.show()

plot(ax=None, *, name=None, plot_chance_level=False, chance_level_kw=None, **kwargs)

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 estimator_name if not None, otherwise no labeling is shown.

plot_chance_levelbool, default=False

Whether to plot the chance level.

New in version 1.3.

chance_level_kwdict, default=None

Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

New in version 1.3.

**kwargsdict

Keyword arguments to be passed to matplotlib’s plot.

Returns:

displayRocCurveDisplay

Object that stores computed values.

Examples using sklearn.metrics.RocCurveDisplay

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Visualizations with Display Objects

Visualizations with Display Objects

Examples using sklearn.metrics.RocCurveDisplay.from_estimator

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

ROC Curve with Visualization API

ROC Curve with Visualization API

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve

Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation

Examples using sklearn.metrics.RocCurveDisplay.from_predictions

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)