LADTree和REPTree分类器在信用风险预测中的能力比较
Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast
摘要：
预测信用违约者是银行等金融行业的一项危险任务。在发放贷款之前确定非付款人是银行家的一项重要任务，也是一项充满冲突的任务。分类技术是预测分析的更好选择，比如找到索赔人，无论他/她是一个谦逊的客户还是一个骗子。对于任何一个像银行家这样的实业家来说，定义杰出的分类器都是一项冒险的任务。这允许计算机科学研究人员通过评估不同的分类器并找出用于此类预测问题的最佳分类器来深入研究有效的研究工作。本文研究了LADTree分类器和REPTree分类器在信用风险预测中的效率，并通过各种措施比较了它们的适应性。采用德国信用数据集，借助开源的机器学习工具进行信用风险预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
