A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

Yannick Kiffen; Francesco Lelli; Omid Feyli

doi:10.20944/preprints202101.0156.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 January 2021 / Approved: 8 January 2021 / Online: 8 January 2021 (13:04:44 CET)

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.

https://francescolelli.info/: Francesco Lelli Author's website

dataset; comparison; algorithm; Naïve Bayes; C5.0 Decision Tree; student enrolment

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A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

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