源代码为：import numpy as np from sklearn import svm from sklearn.model_selection import train_test_split # 将 'b' 和 'm' 转化为数字 0 和 1 def label_transform(label): if label == "'b'": return 0 else: return 1 # 读取数据集 data = [] labels = [] with open('breast-cancer.csv', 'r') as f: for line in f: items = line.strip().split(',') labels.append(label_transform(items[1])) data.append([int(x) for x in items[2:]]) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42) # 训练 SVM 模型 clf = svm.SVC(kernel='linear', C=1) clf.fit(X_train, y_train) # 在测试集上测试模型性能 accuracy = clf.score(X_test, y_test) print(f"SVM模型在测试集上的准确率为：{accuracy}")错误为：Traceback (most recent call last): File "C:\Users\雪恋殇\Desktop\项目相关文献\ch11_二手房房价分析与预测系统\Test0612.py", line 34, in <module> clf.fit(X_train, y_train) File "D:\python\lib\site-packages\sklearn\svm\_base.py", line 199, in fit y = self._validate_targets(y) File "D:\python\lib\site-packages\sklearn\svm\_base.py", line 722, in _validate_targets % len(cls) ValueError: The number of classes has to be greater than one; got 1 class 请给出改正后的代码

根据错误提示，我们需要检查数据集的标签是否正确。在标签转换函数中，将 'b' 转换为数字 0，将其他标签转换为数字 1，但是数据集中只有一种标签，这就导致了训练模型时无法识别标签，从而出现了错误。

可以通过查看数据集来确认标签的情况，如果数据集中只有一种标签，需要重新选择数据集或者重新定义标签转换函数。

以下是修改后的代码：

import numpy as np 
from sklearn import svm 
from sklearn.model_selection import train_test_split  

# 将 'M' 和 'B' 转化为数字 0 和 1   
def label_transform(label):     
    if label == 'B':         
        return 0     
    else:         
        return 1  

# 读取数据集   
data = [] 
labels = [] 
with open('breast-cancer.csv', 'r') as f:     
    for line in f:         
        items = line.strip().split(',')         
        labels.append(label_transform(items[1]))         
        data.append([int(x) for x in items[2:]])   

# 检查标签是否正确    
if len(set(labels)) < 2:
    print("数据集中只有一种标签，无法训练模型")
else:
    # 划分训练集和测试集 
    X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)  

    # 训练 SVM 模型 
    clf = svm.SVC(kernel='linear', C=1) 
    clf.fit(X_train, y_train)  

    # 在测试集上测试模型性能 
    accuracy = clf.score(X_test, y_test) 
    print(f"SVM模型在测试集上的准确率为：{accuracy}")