EstimatorReport.diagnose#

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

Run checks and return a diagnostic with detected issues.

Checks look for common modeling problems such as overfitting and underfitting. Check codes can be muted per-call via ignore or globally via configuration() with ignore_checks=....

Parameters:

ignorelist of str or tuple of str or None, default=None: Check codes to exclude from the results, e.g. ["SKD001"].

Returns:

DiagnosticDisplay: A display object with an HTML representation, with the full list of detected issues 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()
Diagnostic: 1 issue(s) detected, 2 check(s) ran, 0 ignored.
- [SKD002] Potential underfitting.
...
>>> report.diagnose(ignore=["SKD002"])
Diagnostic: 0 issue(s) detected, 1 check(s) ran, 1 ignored.
- No issues were detected in your report!