sklearn.metrics.make_scorer¶
- sklearn.metrics.make_scorer(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)¶
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 or average_precision and returns a callable that scores an estimator’s output.
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 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.
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.grid_search import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, ... scoring=ftwo_scorer)