RocCurveDisplay (original) (raw)
class sklearn.metrics.RocCurveDisplay(*, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None)[source]#
ROC Curve visualization.
It is recommend to usefrom_estimator orfrom_predictions to create a RocCurveDisplay. All parameters are stored as attributes.
Read more in the User Guide.
Parameters:
fprndarray
False positive rate.
tprndarray
True positive rate.
roc_aucfloat, default=None
Area under ROC curve. If None, the roc_auc score is not shown.
estimator_namestr, default=None
Name of estimator. If None, the estimator name is not shown.
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.
Attributes:
**line_**matplotlib Artist
ROC Curve.
**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
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.
Compute the area under the ROC curve.
Examples
import matplotlib.pyplot as plt import numpy as np from sklearn import metrics y = np.array([0, 0, 1, 1]) pred = np.array([0.1, 0.4, 0.35, 0.8]) fpr, tpr, thresholds = metrics.roc_curve(y, pred) roc_auc = metrics.auc(fpr, tpr) display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, ... estimator_name='example estimator') 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, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]#
Create a ROC Curve display from an estimator.
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 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 ordecision_function as the target response. If set to ‘auto’,predict_proba is tried first and if it does not existdecision_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.
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.
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.
Returns:
displayRocCurveDisplay
The ROC Curve display.
See also
Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_predictions
ROC Curve visualization given the probabilities of scores of a classifier.
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()
classmethod from_predictions(y_true, y_pred, *, sample_weight=None, drop_intermediate=True, pos_label=None, name=None, ax=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]#
Plot ROC curve given the true and predicted values.
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 some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.
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 ROC 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.
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
Additional keywords arguments passed to matplotlib plot function.
Returns:
displayRocCurveDisplay
Object that stores computed values.
See also
Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator
ROC Curve visualization given an estimator and some data.
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_pred = clf.decision_function(X_test) RocCurveDisplay.from_predictions( ... y_test, y_pred) <...> plt.show()
plot(ax=None, *, name=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]#
Plot visualization.
Extra keyword arguments will be passed to matplotlib’s plot.
Parameters:
axmatplotlib axes, default=None
Axes object to plot on. If None, a new figure and axes is created.
namestr, default=None
Name of ROC Curve for labeling. If None, use estimator_name if not None, otherwise no labeling is shown.
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.
Returns:
displayRocCurveDisplay
Object that stores computed values.