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Add scoring metric info in cross_val_score #13445

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Mar 14, 2019
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Add scoring metric info in cross_val_score #13445

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8 changes: 7 additions & 1 deletion sklearn/model_selection/_validation.py
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Expand Up @@ -281,7 +281,13 @@ def cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv='warn',
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
``scorer(estimator, X, y)`` which should return only
a single value.

Similar to :func:`cross_validate`
but only a single metric is permitted.

If None, the estimator's default scorer (if available) is used.
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Just say "the estimator's score method"

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@jnothman I thought this was more consistent with cross_validate documentation here but if you still want me to change, then I will

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I'm happy with it being consistent... I'd be more happy with them all being changed :) maybe in a subsequent PR?

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@jnothman a separate PR is best as it hits files other than _validation.py as well, such as scorer.py in sklearn/metrics/ and _search.py in the same folder. I can get started on that PR if you can merge this one as it may conflict with this file.


cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
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