在我的Y值中，我有4个类。因此，显然这是一个多类分类问题。
我正在使用MultinomialNB作为模型。而且我正在做10倍交叉验证，但是，我在Precision，Recall和F1上得到了相同的价值。
这是我的代码：
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.naive_bayes import MultinomialNB
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = train_test_split(yelp_joined_data, Y_label_encoded)
X_train=scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
# fix random seed
seed = 7
numpy.random.seed(seed)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
clf = OneVsRestClassifier(MultinomialNB(alpha=0.01))
prec_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='precision_micro')
prec_res_test.mean() ## value coming as 0.5090949570838452
recall_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='recall_micro')
recall_res_test.mean() # value coming as 0.5090949570838452
rf1_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='f1_micro')
rf1_res_test.mean() # value coming as 0.5090949570838452
在这里，我不能使用通常的精度作为scoring参数，因为这是一个多类问题。
有人可以在我做错事情的地方帮助我吗？