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