我的数据集大小为42542 x 14，我正在尝试构建不同的模型，例如逻辑回归，KNN，RF，决策树，并比较精度。
对于每种型号，我得到的都是高精度但ROC AUC较低。
数据包含约85％的目标变量= 1的样本和15％的目标变量0的样本。我尝试采集样本以处理这种不平衡现象，但仍然得出相同的结果。
glm的系数如下：
glm(formula = loan_status ~ ., family = "binomial", data = lc_train)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7617 0.3131 0.4664 0.6129 1.6734
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -8.264e+00 8.338e-01 -9.911 < 2e-16 ***
annual_inc 5.518e-01 3.748e-02 14.721 < 2e-16 ***
home_own 4.938e-02 3.740e-02 1.320 0.186780
inq_last_6mths1 -2.094e-01 4.241e-02 -4.938 7.88e-07 ***
inq_last_6mths2-5 -3.805e-01 4.187e-02 -9.087 < 2e-16 ***
inq_last_6mths6-10 -9.993e-01 1.065e-01 -9.380 < 2e-16 ***
inq_last_6mths11-15 -1.448e+00 3.510e-01 -4.126 3.68e-05 ***
inq_last_6mths16-20 -2.323e+00 7.946e-01 -2.924 0.003457 **
inq_last_6mths21-25 -1.399e+01 1.970e+02 -0.071 0.943394
inq_last_6mths26-30 1.039e+01 1.384e+02 0.075 0.940161
inq_last_6mths31-35 -1.973e+00 1.230e+00 -1.604 0.108767
loan_amnt -1.838e-05 3.242e-06 -5.669 1.43e-08 ***
purposecredit_card 3.286e-02 1.130e-01 0.291 0.771169
purposedebt_consolidation -1.406e-01 1.032e-01 -1.362 0.173108
purposeeducational -3.591e-01 1.819e-01 -1.974 0.048350 *
purposehome_improvement -2.106e-01 1.189e-01 -1.771 0.076577 .
purposehouse -3.327e-01 1.917e-01 -1.735 0.082718 .
purposemajor_purchase -7.310e-03 1.288e-01 -0.057 0.954732
purposemedical -4.955e-01 1.530e-01 -3.238 0.001203 **
purposemoving -4.352e-01 1.636e-01 -2.661 0.007800 **
purposeother -3.858e-01 1.105e-01 -3.493 0.000478 ***
purposerenewable_energy -8.150e-01 3.036e-01 -2.685 0.007263 **
purposesmall_business -9.715e-01 1.186e-01 -8.191 2.60e-16 ***
purposevacation -4.169e-01 2.012e-01 -2.072 0.038294 *
purposewedding 3.909e-02 1.557e-01 0.251 0.801751
open_acc -1.408e-04 4.147e-03 -0.034 0.972923
gradeB -4.377e-01 6.991e-02 -6.261 3.83e-10 ***
gradeC -5.858e-01 8.340e-02 -7.024 2.15e-12 ***
gradeD -7.636e-01 9.558e-02 -7.990 1.35e-15 ***
gradeE -7.832e-01 1.115e-01 -7.026 2.13e-12 ***
gradeF -9.730e-01 1.325e-01 -7.341 2.11e-13 ***
gradeG -1.031e+00 1.632e-01 -6.318 2.65e-10 ***
verification_statusSource Verified 6.340e-02 4.435e-02 1.429 0.152898
verification_statusVerified 6.864e-02 4.400e-02 1.560 0.118739
dti -4.683e-03 2.791e-03 -1.678 0.093373 .
fico_range_low 6.705e-03 9.292e-04 7.216 5.34e-13 ***
term 5.773e-01 4.499e-02 12.833 < 2e-16 ***
emp_length2-4 years 6.341e-02 4.911e-02 1.291 0.196664
emp_length5-9 years -3.136e-02 5.135e-02 -0.611 0.541355
emp_length10+ years -2.538e-01 5.185e-02 -4.895 9.82e-07 ***
delinq_2yrs2+ 5.919e-02 9.701e-02 0.610 0.541754
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 25339 on 29779 degrees of freedom
Residual deviance: 23265 on 29739 degrees of freedom
AIC: 23347
Number of Fisher Scoring iterations: 10
LR的混淆矩阵如下：
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 32 40
1 1902 10788
Accuracy : 0.8478
95% CI : (0.8415, 0.854)
No Information Rate : 0.8485
P-Value [Acc > NIR] : 0.5842
Kappa : 0.0213
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.016546
Specificity : 0.996306
Pos Pred Value : 0.444444
Neg Pred Value : 0.850118
Prevalence : 0.151544
Detection Rate : 0.002507
Detection Prevalence : 0.005642
Balanced Accuracy : 0.506426
'Positive' Class : 0
有什么方法可以改善AUC？
答案 0 :(得分：0)
如果有人提出混淆矩阵并谈论低ROC AUC，则通常意味着他/她已将预测/概率转换为0和1，而ROC AUC公式则不需要-它适用于原始概率，这给出了好得多的结果。如果要获得最佳的AUC值，最好在训练时将其设置为评估指标，这样可以获得比其他指标更好的结果。