DetCurveDisplay (original) (raw)
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_estimatoror 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 theModel 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
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.
For general information regarding scikit-learn visualization tools, see the Visualization Guide. For guidance on interpreting these plots, refer to theModel Evaluation Guide.
Added in version 1.0.
Parameters:
estimatorestimator instance
Fitted classifier or a fitted Pipelinein 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 ordecision_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_trueis 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
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.
For general information regarding scikit-learn visualization tools, see the Visualization Guide. For guidance on interpreting these plots, refer to theModel 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_trueis 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
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.