sklearn.metrics.check_scoring

sklearn.metrics.check_scoring(estimator, scoring=None, *, allow_none=False)

Determine scorer from user options.

A TypeError will be thrown if the estimator cannot be scored.

Parameters:

estimatorestimator object implementing ‘fit’

The object to use to fit the data.

scoringstr or callable, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the provided estimator object’s score method is used.

allow_nonebool, default=False

If no scoring is specified and the estimator has no score function, we can either return None or raise an exception.

Returns:

scoringcallable

A scorer callable object / function with signature scorer(estimator, X, y).

Examples

>>> from sklearn.datasets import load_iris
>>> from sklearn.metrics import check_scoring
>>> from sklearn.tree import DecisionTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> classifier = DecisionTreeClassifier(max_depth=2).fit(X, y)
>>> scorer = check_scoring(classifier, scoring='accuracy')
>>> scorer(classifier, X, y)
0.96...