Version 1
: Received: 7 January 2021 / Approved: 8 January 2021 / Online: 8 January 2021 (13:04:44 CET)
How to cite:
Kiffen, Y.; Lelli, F.; Feyli, O. A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications. Preprints2021, 2021010156. https://doi.org/10.20944/preprints202101.0156.v1
Kiffen, Y.; Lelli, F.; Feyli, O. A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications. Preprints 2021, 2021010156. https://doi.org/10.20944/preprints202101.0156.v1
Kiffen, Y.; Lelli, F.; Feyli, O. A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications. Preprints2021, 2021010156. https://doi.org/10.20944/preprints202101.0156.v1
APA Style
Kiffen, Y., Lelli, F., & Feyli, O. (2021). A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications. Preprints. https://doi.org/10.20944/preprints202101.0156.v1
Chicago/Turabian Style
Kiffen, Y., Francesco Lelli and Omid Feyli. 2021 "A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications" Preprints. https://doi.org/10.20944/preprints202101.0156.v1
Abstract
In this preprint, we introduce a dataset containing students enrolment applications combined with the related result of their filing procedure. The dataset contains 73 variable. Student candidates, at the time of applying for study, fill a web form for filing the procedure. A committee at Tilburg University review each single application and decide if the student is admissible or not. This dataset is suitable for algorithmic studies and has been used in a comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms. They have been used for predicting the decision of the committee in admitting candidates at various bachelor programs. Our analysis shows that, in this particular case, a combination of the approaches outperform a both of them in term of precision and recall.
Business, Economics and Management, Accounting and Taxation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
