import pandas as pd from sklearn.preprocessing import LabelEncoder

读取数据集

data = pd.read_csv('mushrooms.csv')

将分类标签转换为0和1

label_encoder = LabelEncoder() data['class'] = label_encoder.fit_transform(data['class'])

将离散属性进行编码转换

for column in data.columns[1:]: data[column] = LabelEncoder().fit_transform(data[column])

from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(data.drop('class', axis=1), data['class'], test_size=0.3, random_state=42)

from sklearn.svm import SVC

svm = SVC(kernel='rbf', C=10) svm.fit(X_train, y_train) y_pred = svm.predict(X_test)

输出分类报告和混淆矩阵

print('Classification Report:\n', classification_report(y_test, y_pred)) print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred))

import seaborn as sns import matplotlib.pyplot as plt

绘制混淆矩阵热力图

sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap='Blues') plt.title('Confusion Matrix') plt.xlabel('Predicted Class') plt.ylabel('True Class') plt.show()

绘制分类报告柱状图

report = classification_report(y_test, y_pred, output_dict=True) report_df = pd.DataFrame(report).transpose() report_df.drop('support', axis=1, inplace=True) report_df.plot(kind='bar', rot=0) plt.title('Classification Report') plt.xlabel('Class') plt.ylabel('Score') plt.show()