DetCurveDisplay#

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

Detection Error Tradeoff (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.

Added 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()

classmethod from_estimator(estimator, X, y, *, sample_weight=None, drop_intermediate=True, 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.

Added 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.

drop_intermediatebool, default=True

Whether to drop thresholds where true positives (tp) do not change from the previous or subsequent threshold. All points with the same tp value have the same fnr and thus same y coordinate.

Added in version 1.7.

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()

classmethod from_predictions(y_true, y_pred, *, sample_weight=None, drop_intermediate=True, 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.

Added 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 thresholds where true positives (tp) do not change from the previous or subsequent threshold. All points with the same tp value have the same fnr and thus same y coordinate.

Added in version 1.7.

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()

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.

Gallery examples#

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve