sklearn.metrics.make_scorer()

score_func : callable,

Score function (or loss function) with signature score_func(y, y_pred, **kwargs).

greater_is_better : boolean, 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.

needs_proba : boolean, default=False

Whether score_func requires predict_proba to get probability estimates out of a classifier.

needs_threshold : boolean, default=False

Whether score_func takes a continuous decision certainty. This only works for binary classification using estimators that have either a decision_function or predict_proba method.

For example average_precision or the area under the roc curve can not be computed using discrete predictions alone.

**kwargs : additional arguments

Additional parameters to be passed to score_func.