TITLE:
Logistic and SVM Credit Score Models Based on Lasso Variable Selection
AUTHORS:
Qingqing Li
KEYWORDS:
Credit Evaluation, Logistic Algorithm, SVM Algorithm, Lasso Variable Selection
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.7 No.5,
May
27,
2019
ABSTRACT:
There are many factors influencing personal credit.
We introduce Lasso technique to personal credit evaluation, and establish
Lasso-logistic, Lasso-SVM and Group lasso-logistic models respectively.
Variable selection and parameter estimation are also conducted simultaneously.
Based on the personal credit data set from a certain lending platform, it can
be concluded through experiments that compared with the full-variable Logistic
model and the stepwise Logistic model, the variable selection ability of Group
lasso-logistic model was the strongest, followed by Lasso-logistic and
Lasso-SVM respectively. All three models based on Lasso variable selection have
better filtering capability than stepwise selection. In the meantime, the Group
lasso-logistic model can eliminate or retain relevant virtual variables as a
group to facilitate model interpretation. In terms of prediction accuracy,
Lasso-SVM had the highest prediction accuracy for default users in the training
set, while in the test set, Group lasso-logistic had the best classification
accuracy for default users. Whether in the training set or in the test set, the
Lasso-logistic model has the best classification accuracy for non-default
users. The model based on Lasso variable selection can also better screen out
the key factors influencing personal credit risk.