Credit Scoring Using Ensemble Machine Learning

Abstract

In this study, we applied ensemble machine learning to evaluate credit scoring. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using all 14 features, using selected 6 features on Australian credit data form UCI data set. Results showed that in experiments with all features improved performance was achieved by ensemble learning. The best result was obtained in adaboost CART with 14 features, in which the overall correct rate increases from 83.25% to 85.86%.