Mean error (not squared) in scikit-learn cross_val_score I need to know if the values generated by each fold of cross_val_score have a distribution which is centered on zero.  Something as simple as the median or mean of y_true - y_predicted would suffice.  All I see in the available options are absolute and squared.  I've looked into make scorer but can't see how to code the simple mean error and then call it as the scoring argument in cross_val_score.
from sklearn.datasets import load_diabetes
from sklearn.metrics import make_scorer
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score

def mean_error(y, y_pred):
    # assuming y and y_pred are numpy arrays
    return np.mean(y_pred - y)

X, y = load_diabetes(return_X_y=True)
mean_error_scorer = make_scorer(mean_error, greater_is_better=False)

regr = LinearRegression()
cross_val_score(regr, X, y, scoring=mean_error_scorer)