Credit risk classification using Kernel Logistic Regression with optimal parameter

Recently, Machine Learning techniques have become very popular because of its effectiveness. This study, applies Kernel Logistic Regression (KLR) to the credit risk classification in an attempt to suggest a model with better classification accuracy. Credit risk classification is an interesting and important data mining problem in financial analysis domain. In this study, the optimal parameter values (regularization and kernel function) of KLR. are found by using a grid search technique with 5-fold cross-validation. Credit risk data sets from UCI machine learning are used in order to verify the effectiveness of the KLR method in classifying credit risk. The experiment results show that KLR has promising performance when compared with other Machine Learning techniques in previous research literatures.