如何使用 for 循环在决策树上正确实施装袋？

Question

我正在尝试使用决策树和 for 循环来实现装袋和投票。我正在使用 sklearn 重采样。但是，我得到 Number of labels=97 does not match number of samples=77 并且我可以理解为什么，但我不确定如何解决它。

数据集中有 150 个样本。有150个标签所以 150 * 0.35 = 97 和 97 * 0.8 = 77。 X是长度为150的特征矩阵，并且 y是长度为150

的标签向量

下面是我的代码

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.utils import resample


X_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)

Answer 1

您还应该对标签重新采样并在 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)

如何使用 for 循环在决策树上正确实施装袋？

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