Logistic and SVM Credit Score Models Based on Lasso Variable Selection ()
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.
Share and Cite:
Li, Q. (2019) Logistic and SVM Credit Score Models Based on Lasso Variable Selection.
Journal of Applied Mathematics and Physics,
7, 1131-1148. doi:
10.4236/jamp.2019.75076.