问题描述
我正在训练一个 RandomForestClassifier (sklearn) 来预测信用卡欺诈.当我测试模型并检查 rocauc 分数时，当我使用 roc_auc_score 和 plot_roc_curve 时，我得到了不同的值.roc_auc_score 给我大约 0.89，而 plot_curve 计算出的 AUC 为 0.96，这是为什么?
I am training a RandomForestClassifier (sklearn) to predict credit card fraud. When I then test the model and check the rocauc score i get different values when I use roc_auc_score and plot_roc_curve. roc_auc_score gives me around 0.89 and the plot_curve calculates AUC to 0.96 why is that?
标签都是 0 和 1，预测是 0 或 1.代码:
The labels are all 0 and 1 as well as the predictions are 0 or 1.CodE:
clf = RandomForestClassifier(random_state =42)
clf.fit(X_train, y_train[target].values)
pred_test = clf.predict(X_test)
print(roc_auc_score(y_test, pred_test))
clf_disp = plot_roc_curve(clf, X_test, y_test)
plt.show()
代码的输出(roc_auc_Score 就在图表上方).
Output of the code (the roc_auc_Score is just above the graph).
推荐答案
ROC 曲线和 roc_auc_score 将预测概率作为输入，但正如我从您的代码中看到的，您提供的是预测标签.你需要解决这个问题.
The ROC Curve and the roc_auc_score take the prediction probabilities as input, but as I can see from your code you are providing the prediction labels. You need to fix that.
这篇关于不同的结果 roc_auc_score 和 plot_roc_curve的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！