我试图使用决策树和for循环来实现装袋和投票。我使用了sklearn的resample函数。然而，我得到了Number of labels=97 does not match number of samples=77的错误，我大致能理解为什么会这样，但不确定如何修复它。

数据集中有150个样本。标签也有150个，所以150 * 0.35 = 97，并且97 * 0.8 = 77。X是长度为150的特征矩阵，y是长度为150的标签向量。

以下是我的代码

from sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.utils import resampleX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35, random_state=3)predictions = []for i in range(1,20):    bootstrap_size = int(0.8*len(X_train))    bag = resample(X_train, n_samples = bootstrap_size , random_state=i , replace = True)     Base_DecisionTree = DecisionTreeClassifier(random_state=3)    Base_DecisionTree.fit(bag, y_train)    y_predict = Base_DecisionTree.predict(X_test)    accuracy = accuracy_score(y_test, y_predict)    predictions.append(accuracy)

回答：

你应该同时对标签进行重采样，并在fit()中使用它。

x_bag, y_bag = resample(X_train, y_train, n_samples = bootstrap_size , random_state=i , replace = True) tree = DecisionTreeClassifier(random_state=3)tree.fit(x_bag, y_bag)