check_scoring#

sklearn.metrics.check_scoring(estimator=None, scoring=None, *, allow_none=False)[source]#

Determine scorer from user options.

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

Parameters:

estimatorestimator object implementing ‘fit’ or None, default=None

The object to use to fit the data. If None, then this function may error depending on allow_none.

scoringstr, callable, list, tuple, or dict, default=None

Scorer to use. If scoring represents a single score, one can use:

a single string (see The scoring parameter: defining model evaluation rules);
a callable (see Defining your scoring strategy from metric functions) that returns a single value.

If scoring represents multiple scores, one can use:

a list or tuple of unique strings;
a callable returning a dictionary where the keys are the metric names and the values are the metric scorers;
a dictionary with metric names as keys and callables a values.

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...