我试图在这里进行多类分类，交叉验证的得分都是nan
下面的代码非常适合二进制分类
当我只保留accuracy和balanced_accuracy时，它显示实际得分，当我添加f1或precison或recall时，所有得分都变成nan
我的代码在二进制分类中工作得很好，而我使用的是同一个数据集，这个问题只是改变了目标数据

scoring = {'accuracy': 'accuracy',
          "balanced_accuracy": "balanced_accuracy",
         "precision": "precision",
          "recall": "recall",
          "f1" :"f1",
          "roc_auc":"roc_auc" }
# load the dataset
def load_dataset(df):
    # load the dataset as a numpy array
    data = df
    # retrieve numpy array
    data = data.values
    # split into input and output elements
    X, y = data[:, :-1], data[:, -1]
    y = LabelEncoder().fit_transform(y)
    return X, y
 
# evaluate a model
def evaluate_model(X, y, model):
    # define evaluation procedure
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    # evaluate model
    scores = cross_validate(model, X, y, scoring=scoring, cv=cv, n_jobs=-1)
    return scores
 

model=DecisionTreeClassifier()
 
# define the location of the dataset

# load the dataset
X, y = load_dataset(df2)

# evaluate the model and store results
results_without_nlp = evaluate_model(X, y, model)

我试过用那些
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score但这似乎没有帮助