The Feature selection and Comparison performance of Student’s academic between Random Forest, Naïve bayes and XGboost-Reference-Cited by-同舟云学术

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The Feature selection and Comparison performance of Student’s academic between Random Forest, Naïve bayes and XGboost

Published:2023-11-28 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)
language:
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Author:

Thanarat Preut¹,Kiatjindarat Waranyoo²,Jareanpon Chatklaw³

Affiliation:

1. Mahasarakham University,Faculty of Informatics,Department of Creative Media,Thailand

2. Terminal 8 Co., Ltd.,Thailand

3. Mahasarakham University,Faculty of Informatics,POLAR Lab, Department of Computer Science,Thailand

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10398281/10398232/10398413.pdf?arnumber=10398413

Reference13 articles.

1. Educational Data Mining: A Review

2. Feature dimensionality reduction: a review

3. The prevention and handling of the missing data

4. Preprocessing and analyzing educational data set using X-API for improving student's performance

5. Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

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