RocCurveDisplay#

class skore.RocCurveDisplay(*, roc_curve, roc_auc, pos_label, data_source, ml_task, report_type)[source]#

ROC Curve visualization.

An instance of this class is should created by EstimatorReport.metrics.roc(). You should not create an instance of this class directly.

Parameters:

roc_curveDataFrame: The ROC curve data to display. The columns are - “estimator_name” - “split_index” (may be null) - “label” - “threshold” - “fpr” - “tpr”.
roc_aucDataFrame: The ROC AUC data to display. The columns are - “estimator_name” - “split_index” (may be null) - “label” - “roc_auc”.
pos_labelint, float, bool, str or None: The class considered as positive. Only meaningful for binary classification.
data_source{“train”, “test”, “X_y”}: The data source used to compute the ROC curve.
ml_task{“binary-classification”, “multiclass-classification”}: The machine learning task.
report_type{“comparison-cross-validation”, “comparison-estimator”, “cross-validation”, “estimator”}: The type of report.

Attributes:

ax_matplotlib axes or array of axes: The axes on which the ROC curve is plotted.
figure_matplotlib figure: The figure on which the ROC curve is plotted.
lines_list of matplotlib lines: The lines of the ROC curve.
chance_level_matplotlib line or list of lines or None: The chance level line.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.metrics.roc()
>>> display.plot(roc_curve_kwargs={"color": "tab:red"})

help()[source]#: Display available attributes and methods using rich.

plot(*, estimator_name=None, roc_curve_kwargs=None, plot_chance_level=True, chance_level_kwargs=None, despine=True)[source]#

Plot visualization.

Extra keyword arguments will be passed to matplotlib’s plot.

Parameters:

estimator_namestr, default=None: Name of the estimator used to plot the ROC curve. If None, we use the inferred name from the estimator.
roc_curve_kwargsdict or list of dict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the ROC curve(s).
plot_chance_levelbool, default=True: 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=True: Whether to remove the top and right spines from the plot.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.metrics.roc()
>>> display.plot(roc_curve_kwargs={"color": "tab:red"})