sklearn.metrics.plot_det_curve

sklearn.metrics.plot_det_curve(estimator, X, y, *, sample_weight=None, response_method='auto', name=None, ax=None, pos_label=None, **kwargs)

DEPRECATED: Function plot_det_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: DetCurveDisplay.from_predictions or DetCurveDisplay.from_estimator.

Plot detection error tradeoff (DET) curve.

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

Read more in the User Guide.

New in version 0.24.

Deprecated since version 1.0: plot_det_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the following class methods: from_predictions or from_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.

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

Specifies whether to use predict_proba or decision_function as the predicted 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 DET 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 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.

**kwargsdict

Additional keywords arguments passed to matplotlib plot function.

Returns:

displayDetCurveDisplay

Object that stores computed values.

See also

det_curve

Compute error rates for different probability thresholds.

DetCurveDisplay

DET curve visualization.

DetCurveDisplay.from_estimator

Plot DET curve given an estimator and some data.

DetCurveDisplay.from_predictions

Plot DET curve given the true and predicted labels.

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.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import plot_det_curve
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(n_samples=1000, random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.4, random_state=0)
>>> clf = SVC(random_state=0).fit(X_train, y_train)
>>> plot_det_curve(clf, X_test, y_test)  
<...>
>>> plt.show()