CrossValidationReport.diagnose#

CrossValidationReport.diagnose(*, ignore=None)[source]

Run diagnostics and return a summary of detected issues.

Diagnostics check for common modeling problems such as overfitting and underfitting. Codes can be muted per-call via ignore or globally via )() .

Parameters:
ignorelist of str or tuple of str or None, default=None

Diagnostic codes to exclude from the results, e.g. ["SKD001"].

Returns:
DiagnosticsDisplay

A display object with an HTML representation, with the full diagnostic results accessible via the frame() method.

Examples

>>> from skore import evaluate
>>> from sklearn.dummy import DummyClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(random_state=42)
>>> report = evaluate(DummyClassifier(), X, y, splitter=0.2)
>>> report.diagnose()
Diagnostics: 1 issue(s) detected, 2 check(s) ran, 0 ignored.
- [SKD002] Potential underfitting. Train/test scores are on par and not
significantly better than the dummy baseline for 8/8 comparable metrics. Read
our documentation for more details:
https://docs.skore.probabl.ai/dev/user_guide/diagnostics.html#skd002-underfitting.
Mute with `ignore=['SKD002']`.
>>> report.diagnose(ignore=["SKD002"])
Diagnostics: 0 issue(s) detected, 1 check(s) ran, 1 ignored.
- No issues were detected in your report!

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