EstimatorReport.metrics.roc#

EstimatorReport.metrics.roc(*, data_source='test', X=None, y=None, pos_label=None)[source]#

Plot the ROC curve.

Parameters:
data_source{“test”, “train”, “X_y”}, default=”test”

The data source to use.

  • “test” : use the test set provided when creating the report.

  • “train” : use the train set provided when creating the report.

  • “X_y” : use the provided X and y to compute the metric.

Xarray-like of shape (n_samples, n_features), default=None

New data on which to compute the metric. By default, we use the validation set provided when creating the report.

yarray-like of shape (n_samples,), default=None

New target on which to compute the metric. By default, we use the target provided when creating the report.

pos_labelint, float, bool or str, default=None

The positive class.

Returns:
RocCurveDisplay

The ROC curve display.

Examples

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