A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set
Abstract:For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate… Show more
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