add

CrossValidationReport.metrics.add(metric, *, name=None, response_method='predict', greater_is_better=True, **kwargs)

Add a custom metric to be included in summarize() by default.

Parameters:

metricstr, sklearn scorer, or callable

The metric to add.

namestr, optional

Custom name for the metric. If not provided, the name is inferred from the metric (e.g. the function’s __name__).

response_methodstr or list of str, default=”predict”

Estimator method to get predictions (only for callables).

greater_is_betterbool, default=True

Whether higher values are better (only for callables).

**kwargsAny

Default keyword arguments passed to the score function at call time. Only used when metric is a plain callable.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.metrics import make_scorer, mean_absolute_error
>>> from skore import evaluate
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = evaluate(classifier, X, y, splitter=2, pos_label=1)
>>> report.metrics.add(
...     make_scorer(mean_absolute_error, response_method="predict")
... )
>>> report.metrics.summarize().frame()
                    LogisticRegression
                        mean       std
Metric
...
Mean Absolute Error      ...       ...