make_scorer (original) (raw)
sklearn.metrics.make_scorer(score_func, *, response_method='default', greater_is_better=True, **kwargs)[source]#
Make a scorer from a performance metric or loss function.
A scorer is a wrapper around an arbitrary metric or loss function that is called with the signature scorer(estimator, X, y_true, **kwargs).
It is accepted in all scikit-learn estimators or functions allowing a scoringparameter.
The parameter response_method allows to specify which method of the estimator should be used to feed the scoring/loss function.
Read more in the User Guide.
Parameters:
score_funccallable
Score function (or loss function) with signaturescore_func(y, y_pred, **kwargs).
response_method{“predict_proba”, “decision_function”, “predict”} or list/tuple of such str, default=None
Specifies the response method to use get prediction from an estimator (i.e. predict_proba, decision_function orpredict). Possible choices are:
- if
str, it corresponds to the name to the method to return; - if a list or tuple of
str, it provides the method names in order of preference. The method returned corresponds to the first method in the list and which is implemented byestimator. - if
None, it is equivalent to"predict".
Added in version 1.4.
Deprecated since version 1.6: None is equivalent to ‘predict’ and is deprecated. It will be removed in version 1.8.
greater_is_betterbool, default=True
Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the score_func.
**kwargsadditional arguments
Additional parameters to be passed to score_func.
Returns:
scorercallable
Callable object that returns a scalar score; greater is better.
Examples
from sklearn.metrics import fbeta_score, make_scorer ftwo_scorer = make_scorer(fbeta_score, beta=2) ftwo_scorer make_scorer(fbeta_score, response_method='predict', beta=2) from sklearn.model_selection import GridSearchCV from sklearn.svm import LinearSVC grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer)