from sklearn.decomposition import PCA
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn import metrics

regressor = svm.SVC()

parameters = {‘kernel’:[‘rbf’],
‘C’:[0.01,0.03,0.1,0.3,1,3,10],
‘gamma’:[1e-4,3e-4,1e-3,3e-3,0.01,0.03,0.1,0.3,1]}

clf = GridSearchCV(regressor,parameters,scoring=[‘roc_auc’],refit=‘roc_auc’,cv=10,n_jobs=-1)

clf.fit(np.array(train),np.array(target))

看了文档，也百度了，填refit='precision_score’也不对 填false，true，都不行都是报错

For multi-metric scoring, the parameter refit must be set to a scorer key or a callable to refit an estimator with the best parameter setting on the whole data and make the best_* attributes available for that metric. If this is not needed, refit should
应该怎么填啊，跪求帮助了