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 from_estimator or from_predictions to create a RocCurveDisplay. All parameters are stored as attributes.

For general information regarding scikit-learn visualization tools, see the Visualization Guide. For guidance on interpreting these plots, refer to the Model Evaluation 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.

Added 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.

Added 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_true = np.array([0, 0, 1, 1])
>>> y_score = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score)
>>> 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()

../../_images/sklearn-metrics-RocCurveDisplay-1.png

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, despine=False, **kwargs)[source]#

Create a ROC Curve display from an estimator.

For general information regarding scikit-learn visualization tools, see the Visualization Guide. For guidance on interpreting these plots, refer to the Model Evaluation Guide.

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 thresholds where the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points.
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.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
**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()

../../_images/sklearn-metrics-RocCurveDisplay-2.png

classmethod from_predictions(y_true, y_score=None, *, sample_weight=None, drop_intermediate=True, pos_label=None, name=None, ax=None, plot_chance_level=False, chance_level_kw=None, despine=False, y_pred='deprecated', **kwargs)[source]#

Plot ROC curve given the true and predicted values.

For general information regarding scikit-learn visualization tools, see the Visualization Guide. For guidance on interpreting these plots, refer to the Model Evaluation Guide.

Added in version 1.0.

Parameters:

y_truearray-like of shape (n_samples,): True labels.
y_scorearray-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).

Added in version 1.7: y_pred has been renamed to y_score.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.
drop_intermediatebool, default=True: Whether to drop thresholds where the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points.
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.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
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).

Deprecated since version 1.7: y_pred is deprecated and will be removed in 1.9. Use y_score instead.
**kwargsdict: Additional keywords arguments passed to matplotlib plot function.

Returns:

displayRocCurveDisplay: Object that stores computed values.

Gallery examples#

Feature transformations with ensembles of trees

Visualizations with Display Objects

Evaluation of outlier detection estimators

ROC Curve with Visualization API

Post-tuning the decision threshold for cost-sensitive learning

Detection error tradeoff (DET) curve

Multiclass Receiver Operating Characteristic (ROC)

Receiver Operating Characteristic (ROC) with cross validation

Release Highlights for scikit-learn 0.22

RocCurveDisplay#

Gallery examples#

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