Loan Default Prediction on Large Imbalanced Data Using Random Forests

In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. We compare the performance of our proposed algorithm and the original one on loan default prediction datasets. We also use these two algorithms to create two kinds of balanced random forests to deal with imbalanced data problem. Experiments results show that our proposed algorithm beats the original random forest in terms of both balanced and overall accuracy metrics. Experiments also show that parallel random forests can greatly improve random forests’ efficiency during the learning process.