DetCurveDisplay#

class sklearn.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.

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

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.

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. 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, drop_intermediate=True, pos_label=None, name=None, ax=None, **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_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.

Gallery examples#

Detection error tradeoff (DET) curve

DetCurveDisplay#

Gallery examples#

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