Delete pltcode

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
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-# 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()