我在进行机器学习项目时，想对多个算法进行准确性评估。我使用的是这个CSV文件，仅加载了Date、Time和CO列（我在CSV文件中手动重命名了这些列）。在准备好我的训练数据后，我尝试进行评估，但遇到了以下错误：

ValueError: Supported target types are: ('binary', 'multiclass'). Got 'unknown' instead.

用于评估的向量（X_train和Y_train）的形状是：

(9357, 2)(9357,)

类定义如下：

from sklearn.discriminant_analysis import LinearDiscriminantAnalysisfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_scorefrom sklearn.naive_bayes import GaussianNBfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.svm import SVCfrom sklearn.tree import DecisionTreeClassifierclass Models:    test_size: float    random_state: int    def __init__(self, test_size: float = 0.20, random_state: int = 1) -> None:        super().__init__()        self.test_size = test_size        self.random_state = random_state    @staticmethod    def init_models() -> []:        return [            ('LR', LogisticRegression(solver='liblinear', multi_class='ovr')),            ('LDA', LinearDiscriminantAnalysis()),            ('KNN', KNeighborsClassifier()),            ('CART', DecisionTreeClassifier()),            ('NB', GaussianNB()),            ('SVM', SVC(gamma='auto'))        ]    def train(self, x: [], y: []):        x_train, x_validation, y_train, y_validation = train_test_split(x, y, test_size=self.test_size,                                                                        random_state=self.random_state)        return x_train, x_validation, y_train, y_validation    def evaluate(self, x_train: [], y_train: [], splits: int = 10, random_state: int = 1):        results = []        names = []        models = self.init_models()        for name, model in models:            kfold = StratifiedKFold(n_splits=splits, random_state=random_state)            cv_results = cross_val_score(estimator=model, X=x_train, y=y_train, cv=kfold, scoring='accuracy')            results.append(cv_results)            names.append(name)            print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))

我调用这个类的代码如下：

models_helper = Models()array = dataset.valuesX = array[:, 1:3]Y = array[:, 2]prepared = models_helper.train(X, Y)classification = models_helper.evaluate(prepared[0], prepared[2])

回答：

我通过先用cross_val_predict计算预测值，然后使用预测值和y_test通过metrics.accuracy_score来获取分数，从而避免了这个问题。

# 运行请求的算法并返回准确性指标的函数。# 传递sklearn模型作为参数，连同cv值和训练数据。def fit_ml_algo(algo, X_train, y_train, cv):# One Passmodel = algo.fit(X_train, y_train)acc = round(model.score(X_train, y_train) * 100, 2)# Cross Validation train_pred = model_selection.cross_val_predict(algo,                                               X_train,                                               y_train,                                               cv=cv,                                               n_jobs = -1)# Cross-validation accuracy metricacc_cv = round(metrics.accuracy_score(y_train, train_pred) * 100, 2)return train_pred, acc, acc_cv