RocCurveDisplay#

class sklearn.metrics.RocCurveDisplay(*, fpr, tpr, roc_auc=None, name=None, pos_label=None, estimator_name='deprecated')[source]#

ROC Curve visualization.

It is recommended to use from_estimator or from_predictions or from_cv_results to create a RocCurveDisplay. 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.

Parameters:

fprndarray or list of ndarrays: False positive rates. Each ndarray should contain values for a single curve. If plotting multiple curves, list should be of same length as tpr.

Changed in version 1.7: Now accepts a list for plotting multiple curves.
tprndarray or list of ndarrays: True positive rates. Each ndarray should contain values for a single curve. If plotting multiple curves, list should be of same length as fpr.

Changed in version 1.7: Now accepts a list for plotting multiple curves.
roc_aucfloat or list of floats, default=None: Area under ROC curve, used for labeling each curve in the legend. If plotting multiple curves, should be a list of the same length as fpr and tpr. If None, ROC AUC scores are not shown in the legend.

Changed in version 1.7: Now accepts a list for plotting multiple curves.
namestr or list of str, default=None: Name for labeling legend entries. The number of legend entries is determined by the curve_kwargs passed to plot. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with curve_kwargs being a dictionary or None. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with the same name. If None, set to name provided at RocCurveDisplay initialization. If still None, no name is shown in the legend.

Added in version 1.7.
pos_labelint, float, bool or str, default=None: The class considered as the positive class when computing the roc auc metrics. By default, estimators.classes_[1] is considered as the positive class.

Added in version 0.24.
estimator_namestr, default=None: Name of estimator. If None, the estimator name is not shown.

Deprecated since version 1.7: estimator_name is deprecated and will be removed in 1.9. Use name instead.

Attributes:

line_matplotlib Artist or list of matplotlib Artists: ROC Curves.

Changed in version 1.7: This attribute can now be a list of Artists, for when multiple curves are plotted.
chance_level_matplotlib Artist or None: The chance level line. It is None if the chance level is not plotted.

Added in version 1.3.
ax_matplotlib Axes: Axes with ROC Curve.
figure_matplotlib Figure: Figure containing the curve.

See also

roc_curve: Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator: Plot Receiver Operating Characteristic (ROC) curve given an estimator and some data.
RocCurveDisplay.from_predictions: Plot Receiver Operating Characteristic (ROC) curve given the true and predicted values.
roc_auc_score: Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from sklearn import metrics
>>> y_true = np.array([0, 0, 1, 1])
>>> y_score = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score)
>>> roc_auc = metrics.auc(fpr, tpr)
>>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
...                                   name='example estimator')
>>> display.plot()
<...>
>>> plt.show()

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

classmethod from_cv_results(cv_results, X, y, *, sample_weight=None, drop_intermediate=True, response_method='auto', pos_label=None, ax=None, name=None, curve_kwargs=None, plot_chance_level=False, chance_level_kwargs=None, despine=False)[source]#

Create a multi-fold ROC curve display given cross-validation results.

Added in version 1.7.

Parameters:

cv_resultsdict: Dictionary as returned by cross_validate using return_estimator=True and return_indices=True (i.e., dictionary should contain the keys “estimator” and “indices”).
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 some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.
response_method{‘predict_proba’, ‘decision_function’, ‘auto’} default=’auto’: Specifies whether to use predict_proba or decision_function as the 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 class considered as the positive class when computing the ROC AUC metrics. By default, estimators.classes_[1] is considered as the positive class.
axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
namestr or list of str, default=None: Name for labeling legend entries. The number of legend entries is determined by curve_kwargs. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with curve_kwargs being a dictionary or None. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with the same name. If None, no name is shown in the legend.
curve_kwargsdict or list of dict, default=None: Keywords arguments to be passed to matplotlib’s plot function to draw individual ROC curves. If a list is provided the parameters are applied to the ROC curves of each CV fold sequentially and a legend entry is added for each curve. If a single dictionary is provided, the same parameters are applied to all ROC curves and a single legend entry for all curves is added, labeled with the mean ROC AUC score.
plot_chance_levelbool, default=False: Whether to plot the chance level.
chance_level_kwargsdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Returns:

displayRocCurveDisplay: The multi-fold ROC curve display.

See also

roc_curve: Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator: ROC Curve visualization given an estimator and some data.
RocCurveDisplay.from_predictions: ROC Curve visualization given the probabilities of scores of a classifier.
roc_auc_score: Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> from sklearn.model_selection import cross_validate
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> clf = SVC(random_state=0)
>>> cv_results = cross_validate(
...     clf, X, y, cv=3, return_estimator=True, return_indices=True)
>>> RocCurveDisplay.from_cv_results(cv_results, X, y)
<...>
>>> plt.show()

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

classmethod from_estimator(estimator, X, y, *, sample_weight=None, drop_intermediate=True, response_method='auto', pos_label=None, name=None, ax=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]#

Create a ROC Curve display from an estimator.

For general information regarding scikit-learn visualization tools, see the Visualization Guide. For guidance on interpreting these plots, refer to the Model Evaluation Guide.

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 the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points.
response_method{‘predict_proba’, ‘decision_function’, ‘auto’} default=’auto’: Specifies whether to use predict_proba or decision_function as the 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 class considered as the positive class when computing the ROC AUC. By default, estimators.classes_[1] is considered as the positive class.
namestr, default=None: Name of ROC 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.
curve_kwargsdict, default=None: Keywords arguments to be passed to matplotlib’s plot function.

Added in version 1.7.
plot_chance_levelbool, default=False: Whether to plot the chance level.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
**kwargsdict: Keyword arguments to be passed to matplotlib’s plot.

Deprecated since version 1.7: kwargs is deprecated and will be removed in 1.9. Pass matplotlib arguments to curve_kwargs as a dictionary instead.

Returns:

displayRocCurveDisplay: The ROC Curve display.

See also

roc_curve: Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_predictions: ROC Curve visualization given the probabilities of scores of a classifier.
roc_auc_score: Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> clf = SVC(random_state=0).fit(X_train, y_train)
>>> RocCurveDisplay.from_estimator(
...    clf, X_test, y_test)
<...>
>>> plt.show()

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

classmethod from_predictions(y_true, y_score=None, *, sample_weight=None, drop_intermediate=True, pos_label=None, name=None, ax=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, y_pred='deprecated', **kwargs)[source]#

Plot ROC curve given the true and predicted values.

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, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).

Added in version 1.7: 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 the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points.
pos_labelint, float, bool or str, default=None: The label of the positive class when computing the ROC AUC. 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 ROC curve for legend 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.
curve_kwargsdict, default=None: Keywords arguments to be passed to matplotlib’s plot function.

Added in version 1.7.
plot_chance_levelbool, default=False: Whether to plot the chance level.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
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).

Deprecated since version 1.7: y_pred is deprecated and will be removed in 1.9. Use y_score instead.
**kwargsdict: Additional keywords arguments passed to matplotlib plot function.

Deprecated since version 1.7: kwargs is deprecated and will be removed in 1.9. Pass matplotlib arguments to curve_kwargs as a dictionary instead.

Returns:

displayRocCurveDisplay: Object that stores computed values.

See also

roc_curve: Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator: ROC Curve visualization given an estimator and some data.
roc_auc_score: Compute the area under the ROC curve.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, random_state=0)
>>> clf = SVC(random_state=0).fit(X_train, y_train)
>>> y_score = clf.decision_function(X_test)
>>> RocCurveDisplay.from_predictions(y_test, y_score)
<...>
>>> plt.show()

../../_images/sklearn-metrics-RocCurveDisplay-4.png

plot(ax=None, *, name=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]#

Plot visualization.

Parameters:

axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
namestr or list of str, default=None: Name for labeling legend entries. The number of legend entries is determined by curve_kwargs. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with curve_kwargs being a dictionary or None. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with the same name. If None, set to name provided at RocCurveDisplay initialization. If still None, no name is shown in the legend.

Added in version 1.7.
curve_kwargsdict or list of dict, default=None: Keywords arguments to be passed to matplotlib’s plot function to draw individual ROC curves. For single curve plotting, should be a dictionary. For multi-curve plotting, if a list is provided the parameters are applied to the ROC curves of each CV fold sequentially and a legend entry is added for each curve. If a single dictionary is provided, the same parameters are applied to all ROC curves and a single legend entry for all curves is added, labeled with the mean ROC AUC score.

Added in version 1.7.
plot_chance_levelbool, default=False: Whether to plot the chance level.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
**kwargsdict: Keyword arguments to be passed to matplotlib’s plot.

Deprecated since version 1.7: kwargs is deprecated and will be removed in 1.9. Pass matplotlib arguments to curve_kwargs as a dictionary instead.

Returns:

displayRocCurveDisplay: Object that stores computed values.

Gallery examples#

Feature transformations with ensembles of trees

Visualizations with Display Objects

Evaluation of outlier detection estimators

ROC Curve with Visualization API

Post-tuning the decision threshold for cost-sensitive learning

Detection error tradeoff (DET) curve

Multiclass Receiver Operating Characteristic (ROC)

Receiver Operating Characteristic (ROC) with cross validation

Release Highlights for scikit-learn 0.22

RocCurveDisplay#

Gallery examples#

This Page