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pltcode

+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+
+# 假设 y_test 是真实标签，y_pred 是预测标签
+conf_mat = confusion_matrix(y_test, y_pred)
+
+plt.figure(figsize=(10, 7))
+sns.heatmap(conf_mat, annot=True, fmt='d', cmap='YlGnBu')
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.show()
+
+
+from sklearn.metrics import roc_curve, auc
+
+# 假设 y_test 是真实标签，y_score 是预测得分
+fpr, tpr, _ = roc_curve(y_test, y_score)
+roc_auc = auc(fpr, tpr)
+
+plt.figure()
+plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
+plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+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()