以下是一个完整的 Python 代码示例，用于绘制 PR 曲线和 ROC 曲线来比较两个模型的优劣。这里使用逻辑回归和支持向量机作为两个示例模型，并使用乳腺癌数据集进行演示。 ```python import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.metrics import precision_recall_curve, roc_curve, auc import matplotlib.pyplot as plt # 加载乳腺癌数据集 data = load_breast_cancer() X = data.data y = data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建并训练逻辑回归模型 model1 = LogisticRegression(solver='liblinear') model1.fit(X_train, y_train) y_score1 = model1.predict_proba(X_test)[:, 1] # 创建并训练支持向量机模型 model2 = SVC(probability=True) model2.fit(X_train, y_train) y_score2 = model2.predict_proba(X_test)[:, 1] # 绘制 PR 曲线 precision1, recall1, _ = precision_recall_curve(y_test, y_score1) precision2, recall2, _ = precision_recall_curve(y_test, y_score2) plt.figure() plt.plot(recall1, precision1, label='Logistic Regression PR curve') plt.plot(recall2, precision2, label='SVM PR curve') plt.xlabel('Recall') plt.ylabel('Precision') plt.title('Precision-Recall Curve') plt.legend() plt.show() # 绘制 ROC 曲线 fpr1, tpr1, _ = roc_curve(y_test, y_score1) fpr2, tpr2, _ = roc_curve(y_test, y_score2) roc_auc1 = auc(fpr1, tpr1) roc_auc2 = auc(fpr2, tpr2) plt.figure() plt.plot(fpr1, tpr1, label='Logistic Regression ROC curve (area = %0.2f)' % roc_auc1) plt.plot(fpr2, tpr2, label='SVM ROC curve (area = %0.2f)' % roc_auc2) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver Operating Characteristic') plt.legend(loc="lower right") plt.show() ``` ###