Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast

TL;DR

This paper compares the performance of LADTree and REPTree classifiers in predicting credit risk using the German credit dataset, aiming to identify the more effective model for financial risk assessment.

Contribution

It provides a comparative analysis of LADTree and REPTree classifiers specifically for credit risk prediction, which is a novel evaluation in this context.

Findings

LADTree outperforms REPTree in accuracy.

REPTree is faster in training time.

Both classifiers show potential for credit risk prediction.

Abstract

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.