# Using Scorer Object for Classifier Score Method for scikit-learn

I have written my custom scorer object which is necessary for my problem and which I've called "p_value_scoring_object".

For the function sklearn.cross_validation.cross_val_score one of the parameters is "scoring", which allows to use this scorer object.

However, this option is not available for the score method of a classifier. Is sklearn just lacking that feature, or is there a way around it?

from sklearn.datasets import load_iris
from sklearn.cross_validation import cross_val_score
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state=0)
cross_val_score(clf, iris.data, iris.target, cv=10,scoring=p_value_scoring_object)


This works. However, this doesn't:

clf.fit(iris.data,iris.target)
clf.score(iris.data,iris.target,scoring=p_value_scoring_object)


Why should it work? scoring is an argument of cross_val_score, not score. The documentation says score "returns the mean accuracy on the given test data and labels." This behavior is not specific to DecisionTreeClassifier, but common to classifiers in sklearn.