원문정보
국제인공지능학회(구 한국인터넷방송통신학회)
International Journal of Internet, Broadcasting and Communication
Vol.15 No.2
2023.05
pp.261-267
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
A common approach to the problem of predicting student test scores is based on the student's previous educational history. In this study, high school transcripts of about two thousand candidates, who took the High-school Student Assessment (HSA) were collected. The data were estimated through building a regression model - Random Forest and optimizing the model's parameters based on Genetic Algorithm (GA) to predict the HSA scores. The RMSE (Root Mean Square Error) measure of the predictive models was used to evaluate the model’s performance.
목차
Abstract
1. Introduction
2. Machine learning framework to predicts HSA score
3. Experiments and results
3.1. Dataset
3.2. Experimental results and discussions
4. Conclusion
References
1. Introduction
2. Machine learning framework to predicts HSA score
3. Experiments and results
3.1. Dataset
3.2. Experimental results and discussions
4. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보