python - Scikit-learn 的 DecisionTreeClassifier 的 fit 方法将 ValueError : Couldn't broadcast input array from shape (10, 35) 赋予形状 (10)

Question

所以我试图制作一个决策树，我的目标是数组 [0, 1] (二进制“NO”或“YES”)，我的输入训练集是三维数组，第一个元素全部为“NO”示例( 10) 各有 35 个特征，与"is"相同。但我不断收到此错误。

    file1 = open(file1.txt) # examples of 'No' class
    file2 = open(file2.txt) # examples of 'Yes' class
    x = vectorizer.fit_transform(file1)
    y = vectorizer.fit_transform(file2)    

    x_array = x.toarray()    
    y_array = y.toarray()    


    x_train, x_test, y_train, y_test = train_test_split(x_array, y_array, 
    test_size=0.2)    
    target = [0, 1] # 0 encoded as 'No' and 1 as 'Yes
    train = [x_train, y_train]

    decisiontree = DecisionTreeClassifier(random_state=0, max_depth=5)
    decisiontree = decisiontree.fit(train, target)    

感谢您的帮助。

编辑:我正在从 txt 文件加载数据，它是文本数据，我尝试打印数组的某些部分，这里是

[[0 0 0 ... 0 0 0]    
 [0 0 0 ... 0 0 0]     
 [0 0 0 ... 0 0 0]     
 [0 0 0 ... 0 0 0]]    

Answer 1

我认为原因是您对 decisiontree.fit 中的 fit 方法感到困惑。

对于decisiontree.fit(X,Y)，它期望X是数据点，Y是标签。也就是说，如果 X 的形状为 N x 32，则 Y 的形状应为 N(其中 >N 是数据点的数量)。

您应该将x_array和y_array合并为整个数据集，将其拆分，然后使用相应的标签进行fit。

考虑以下因素:

# from sklearn.model_selection import train_test_split
# from sklearn.tree import DecisionTreeClassifier
import numpy as np

file1 = open(file1.txt)
file2 = open(file2.txt)
x = vectorizer.fit_transform(file1)
y = vectorizer.fit_transform(file2)    

x_array = x.toarray()    
y_array = y.toarray()

# ------------------------------------------------------------
# combine the positive and negative examples
data = np.concatenate([x_array, y_array], axis=0)
# create corresponding labels (based on the data's length)
labels = np.concatenate([np.zeros(x_array.shape[0]), 
                          np.ones(y_array.shape[0])], axis=0)

# split into train and test set
train_data, test_data, train_labels, test_labels = train_test_split(
    data, labels, test_size=0.2)

decisiontree = DecisionTreeClassifier(random_state=0, max_depth=5)
decisiontree = decisiontree.fit(train_data, train_labels)

# ------------------------------------------------------------
# this is how you can test model performance with the test set
correct_predictions = np.count_nonzero(
    decisiontree.predict(test_data) == test_labels
  )

print("Correct prediction in test set: {}/{}".format(correct_predictions,
                                                       test_labels.shape[0]))