from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, classification_report, confusion_matrix import matplotlib.pyplot as plt import seaborn as sns # 创建并训练模型 model = LogisticRegression(random_state=42) model.fit(X_train, y_train) # 预测 y_pred = model.predict(X_test) # 评估模型 accuracy = accuracy_score(y_test, y_pred) print(f"模型准确率: {accuracy:.4f}") # 分类报告 print("分类报告:") print(classification_report(y_test, y_pred)) # 混淆矩阵 cm = confusion_matrix(y_test, y_pred) plt.figure(figsize=(8, 6)) sns.heatmap(cm, annot=True, fmt="d", cmap="Blues") plt.title("混淆矩阵") plt.ylabel("实际标签") plt.xlabel("预测标签") plt.show()