Volume 3, Issue 1, March 2018, Page: 1-12
Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients
NH Niloy, Department of Science, Ruhea College, Rangpur, Bangladesh
MAI Navid, Department of Science, Ruhea College, Rangpur, Bangladesh
Received: Oct. 17, 2017;       Accepted: Nov. 1, 2017;       Published: Jan. 10, 2018
DOI: 10.11648/j.ajdmkd.20180301.11      View  1133      Downloads  135
Abstract
Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naïve Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.
Keywords
Machine Learning, Naïve Bayesian Classifier, Decision Trees, Predictive Model
To cite this article
NH Niloy, MAI Navid, Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients, American Journal of Data Mining and Knowledge Discovery. Vol. 3, No. 1, 2018, pp. 1-12. doi: 10.11648/j.ajdmkd.20180301.11
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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