改进代码ValueError Traceback (most recent call last) Input In [15], in <cell line: 26>() 24 y.append(row[-1]) 25 le = LabelEncoder() ---> 26 X = le.fit_transform(X) 27 y = le.fit_transform(y)# 生成决策树 28 clf = DecisionTreeClassifier(criterion='entropy') File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py:115, in LabelEncoder.fit_transform(self, y) 102 def fit_transform(self, y): 103 """Fit label encoder and return encoded labels. 104 105 Parameters (...) 113 Encoded labels. 114 """ --> 115 y = column_or_1d(y, warn=True) 116 self.classes_, y = _unique(y, return_inverse=True) 117 return y File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py:1038, in column_or_1d(y, warn) 1029 warnings.warn( 1030 "A column-vector y was passed when a 1d array was" 1031 " expected. Please change the shape of y to " (...) 1034 stacklevel=2, 1035 ) 1036 return np.ravel(y) -> 1038 raise ValueError( 1039 "y should be a 1d array, got an array of shape {} instead.".format(shape) 1040 ) ValueError: y should be a 1d array, got an array of shape (15, 4) instead.

这段代码出现了一个 ValueError，原因是 y 的形状应当是一维数组，但是在这里 y 的形状是 (15, 4)，也就是一个 $15 \times 4$ 的二维数组。解决这个问题的方法是将 y 转换成一维数组，可以使用 numpy 的 flatten() 方法或者 reshape(-1) 方法将 y 转换成一维数组。修改后的代码如下：

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder

data = np.loadtxt('data.csv', delimiter=',', dtype=str)
X = data[:, :-1]
y = data[:, -1].flatten() # 将 y 转换成一维数组
le = LabelEncoder()
X = le.fit_transform(X)
y = le.fit_transform(y)

clf = DecisionTreeClassifier(criterion='entropy')
clf.fit(X, y)