Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast
This work addresses credit risk prediction for banks, but it is incremental as it applies existing methods to a standard dataset.
The study compared LADTree and REPTree classifiers for predicting credit risk using the German credit dataset, finding that LADTree achieved higher accuracy and other performance metrics than REPTree.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertaining non-payer before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a banker. This allow computer science researchers to drill down efficient research works through evaluating different classifiers and finding out the best classifier for such predictive problems. This research work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction and compares their fitness through various measures. German credit dataset has been taken and used to predict the credit risk with a help of open source machine learning tool.