sklearn.metrics
.check_scoring#
- sklearn.metrics.check_scoring(estimator, 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’
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’sscore
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...