`sklearn.metrics`.plot_roc_curve¶

sklearn.metrics.plot_roc_curve(estimator, X, y, *, sample_weight=None, drop_intermediate=True, response_method='auto', name=None, ax=None, pos_label=None, **kwargs)[source]¶

DEPRECATED: Function plot_roc_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: sklearn.metric.RocCurveDisplay.from_predictions or sklearn.metric.RocCurveDisplay.from_estimator.

Plot Receiver operating characteristic (ROC) curve.

Extra keyword arguments will be passed to matplotlib’s plot.

Read more in the User 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 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.
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.
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.
**kwargsdict: Additional keywords arguments passed to matplotlib plot function.

New in version 0.24.

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.
RocCurveDisplay.from_predictions: ROC Curve visualisation given the true and predicted values.
roc_auc_score: Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn import datasets, metrics, model_selection, svm
>>> X, y = datasets.make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = model_selection.train_test_split(
...     X, y, random_state=0)
>>> clf = svm.SVC(random_state=0)
>>> clf.fit(X_train, y_train)
SVC(random_state=0)
>>> metrics.plot_roc_curve(clf, X_test, y_test) 
<...>
>>> plt.show()

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

Examples using `sklearn.metrics.plot_roc_curve`¶

sklearn.metrics.plot_roc_curve¶

Examples using sklearn.metrics.plot_roc_curve¶

`sklearn.metrics`.plot_roc_curve¶

Examples using `sklearn.metrics.plot_roc_curve`¶