问题
My dataset size is 42542 x 14 and I am trying to build different models like logistic regression, KNN, RF, Decision trees and compare the accuracies.
I get a high accuracy but low ROC AUC for every model.
The data has about 85% samples with target variable = 1 and 15% with target variable 0. I tried taking samples in order to handle this imbalance, but it still gives the same results.
Coeffs for glm are as follow:
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
The confusion matrix for LR is as below:
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
Is there any way I can improve the AUC?
回答1:
If someone presents a confusion matrix and talks about low ROC AUC, it usually means that he/she has converted predictions/probabilities into 0 and 1, while ROC AUC formula does not require that - it works on raw probabilities, what gives much better results. If the aim is to obtain the best AUC value, it is good to set it as an evaluation metric while training, which enables to obtain better results than with other metrics.
来源：https://stackoverflow.com/questions/59398046/why-am-i-getting-good-accuracy-but-low-roc-auc-for-multiple-models