`sklearn.metrics`.DetCurveDisplay#

class sklearn.metrics.DetCurveDisplay(*, fpr, fnr, estimator_name=None, pos_label=None)[source]#

DET curve visualization.

It is recommend to use from_estimator or from_predictions to create a visualizer. All parameters are stored as attributes.

Read more in the User Guide.

New in version 0.24.

Parameters:

fprndarray: False positive rate.
fnrndarray: False negative rate.
estimator_namestr, default=None: Name of estimator. If None, the estimator name is not shown.
pos_labelint, float, bool or str, default=None: The label of the positive class.

Attributes:

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

See also

det_curve: Compute error rates for different probability thresholds.
DetCurveDisplay.from_estimator: Plot DET curve given an estimator and some data.
DetCurveDisplay.from_predictions: Plot DET curve given the true and predicted labels.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import det_curve, DetCurveDisplay
>>> 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)
>>> y_pred = clf.decision_function(X_test)
>>> fpr, fnr, _ = det_curve(y_test, y_pred)
>>> display = DetCurveDisplay(
...     fpr=fpr, fnr=fnr, estimator_name="SVC"
... )
>>> display.plot()
<...>
>>> plt.show()

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

Methods

`from_estimator`(estimator, X, y, *[, ...])	Plot DET curve given an estimator and data.
`from_predictions`(y_true, y_pred, *[, ...])	Plot the DET curve given the true and predicted labels.
`plot`([ax, name])	Plot visualization.

classmethod from_estimator(estimator, X, y, *, sample_weight=None, response_method='auto', pos_label=None, name=None, ax=None, **kwargs)[source]#

Plot DET curve given an estimator and data.

Read more in the User Guide.

New in version 1.0.

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.
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 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.
**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.from_predictions: Plot DET curve given the true and predicted labels.

Examples

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

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

classmethod from_predictions(y_true, y_pred, *, sample_weight=None, pos_label=None, name=None, ax=None, **kwargs)[source]#

Plot the DET curve given the true and predicted labels.

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.
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 DET 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.
**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.from_estimator: Plot DET curve given an estimator and some data.

Examples

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

../../_images/sklearn-metrics-DetCurveDisplay-3.png

plot(ax=None, *, name=None, **kwargs)[source]#

Plot visualization.

Parameters:

axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
namestr, default=None: Name of DET curve for labeling. If None, use estimator_name if it is not None, otherwise no labeling is shown.
**kwargsdict: Additional keywords arguments passed to matplotlib plot function.

Returns:

displayDetCurveDisplay: Object that stores computed values.

Examples using `sklearn.metrics.DetCurveDisplay.from_estimator`#

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve