A Classifier Evaluation for Payments’ Default Predictions in a Brazilian Retail Company

Abstract

This article presents an investigation about the performance of classification algorithms used for predicting payments’ default. Classifiers used for modelling the data set include: Logistic Regression; Naive-Bayes; Decision Trees; Support Vector Machine; k-Nearest Neighbors; Random Forests; and Artificial Neural Networks. These classifiers were applied to both balanced and original data using the Weka data mining tool. Results from experiments revealed that Logistics Regression and Naive Bayes classifiers had the best performance for the chosen data set.