A New Model Averaging Approach in Predicting Credit Risk Default

The paper introduces a novel approach to ensemble modeling as weighted model average technique. proposed idea is prudent, simple understand, and easy implement compared the Bayesian frequentist approach. provides both theoretical empirical contributions for assessing credit risk (probability of default) effectively in new way by creating an linear combination machine learning models. can be generalized any classification problems other domains where ensemble-type subject interest not limited unbalanced dataset or assessment. results suggest better forecasting performance single best well-known parametric, non-parametric, scope our extended further improvement estimating weights differently that may beneficial enhance future research direction.