The team used shallow linear and non-linear classification models, such as support vector machines (SVM), logistic regression with different types of regularization (LASSO, ridge, elastic net ...

Furthermore, SVM divide a set of labelled credit applicants into subsets of 'typical' and 'critical' patterns. The correct class label of a typical pattern is usually very easy to predict, even with ...