问题描述
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.
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.
对于每种型号，我得到的都是高精度，但ROC AUC却很低.
I get a high accuracy but low ROC AUC for every model.
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.我尝试采集样本以解决这种不平衡问题，但仍然得出相同的结果.
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.
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?
Is there any way I can improve the AUC?
推荐答案
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公式并不需要-它可以处理原始概率，从而可以提供更好的结果.如果目标是获得最佳的AUC值，则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.
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.
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