DetCurveDisplay

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

Detection Error Tradeoff (DET) curve visualization.

It is recommended to use from_estimator or from_predictions to create a visualizer. 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.

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. If not None, this value is displayed in the x- and y-axes labels.

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_score = clf.decision_function(X_test)
>>> fpr, fnr, _ = det_curve(y_test, y_score)
>>> display = DetCurveDisplay(
...     fpr=fpr, fnr=fnr, estimator_name="SVC"
... )
>>> display.plot()
<...>
>>> plt.show()

classmethodfrom_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.

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:

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. By default, estimators.classes_[1] is considered as the positive class.

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()

classmethodfrom_predictions(y_true, y_score=None, *, sample_weight=None, drop_intermediate=True, pos_label=None, name=None, ax=None, y_pred='deprecated', **kwargs)[source]

Plot the DET curve given the true and predicted labels.

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 or non-thresholded decision values (as returned by decision_function on some classifiers).

Added in version 1.8: 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 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.

y_predarray-like of shape (n_samples,)

Target scores, can either be probability estimates of the positive class or non-thresholded decision values (as returned by decision_function on some classifiers).

Deprecated since version 1.8: y_pred is deprecated and will be removed in 1.10. Use y_score instead.

**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_score = clf.decision_function(X_test)
>>> DetCurveDisplay.from_predictions(
...    y_test, y_score)
<...>
>>> 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