sklearn.metrics
.make_scorer¶

sklearn.metrics.
make_scorer
(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)[source]¶ Make a scorer from a performance metric or loss function.
This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. It takes a score function, such as
accuracy_score
,mean_squared_error
,adjusted_rand_index
oraverage_precision
and returns a callable that scores an estimator’s output.Read more in the User Guide.
Parameters:  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 signflip 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.
If True, for binary
y_true
, the score function is supposed to accept a 1Dy_pred
(i.e., probability of the positive class, shape(n_samples,)
). 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.
If True, for binary
y_true
, the score function is supposed to accept a 1Dy_pred
(i.e., probability of the positive class or the decision function, shape(n_samples,)
).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.
Returns:  scorer : callable
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, beta=2) >>> from sklearn.model_selection import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer)