ComparisonReport.metrics.log_loss#

ComparisonReport.metrics.log_loss(*, data_source='test', X=None, y=None)[source]#

Compute the log loss.

Parameters:
data_source{“test”, “train”}, 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.

Returns:
pd.DataFrame

The log-loss.

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 ComparisonReport, EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> estimator_1 = LogisticRegression(max_iter=10000, random_state=42)
>>> estimator_report_1 = EstimatorReport(
...     estimator_1,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> estimator_2 = LogisticRegression(max_iter=10000, random_state=43)
>>> estimator_report_2 = EstimatorReport(
...     estimator_2,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> comparison_report = ComparisonReport(
...     [estimator_report_1, estimator_report_2]
... )
>>> comparison_report.metrics.log_loss()
Estimator      LogisticRegression  LogisticRegression
Metric
Log loss                 0.082...            0.082...