The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

Yagyanath Rimal; Navneet Sharma; Abeer Alsadoon

doi:10.1007/s11042-024-18426-2

journal article Feb 15, 2024

The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

Yagyanath Rimal

Navneet Sharma

Abeer Alsadoon

Multimedia Tools and Applications Vol. 83 No. 30 pp. 74349-74364 · Springer Science and Business Media LLC

View at Publisher Save 10.1007/s11042-024-18426-2

Topics

No keywords indexed for this article. Browse by subject →

References

33

[1]

Marcot BG, Hanea AM (2021) What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Comput Stat 36(3):2009–2031. https://doi.org/10.1007/s00180-020-00999-9 10.1007/s00180-020-00999-9

[2]

Doyle T (2023) Helping students learn in a learner-centered environment: a guide to facilitating learning in higher education. Taylor & Francis, New York. https://doi.org/10.4324/9781003445067 10.4324/9781003445067

[3]

Daniels LM, Goegan LD, Parker PC (2021) The impact of COVID-19 triggered changes to instruction and assessment on university students’ self-reported motivation, engagement and perceptions. Soc Psychol Educ 24(1):299–318. https://doi.org/10.1007/s11218-021-09612-3 10.1007/s11218-021-09612-3

[4]

Formative assessment and feedback for learning in higher education: A systematic review

Rebecca Morris, Thomas Perry, Lindsey Wardle

Review of Education 2021 10.1002/rev3.3292

[5]

El Said GR (2021) How did the COVID-19 pandemic affect higher education learning experience? An empirical investigation of learners’ academic performance at a university in a developing country. Adv Hum-Comput Interact 2021:1–10 10.1155/2021/6649524

[6]

Hurlbut AR (2018) Online vs. traditional learning in teacher education: a comparison of student progress. Am J Distance Educ 32(4):248–266. https://doi.org/10.1080/08923647.2018.1509265 10.1080/08923647.2018.1509265

[7]

Pepple DJ, Young LE, Carroll RG (2010) A comparison of student performance in multiple-choice and long essay questions in the MBBS stage I physiology examination at the University of the West Indies (Mona Campus). Adv Physiol Educ 34(2):86–89. https://doi.org/10.1152/advan.00087.2009 10.1152/advan.00087.2009

[8]

Bujang SDA et al (2021) Multiclass Prediction Model for Student Grade Prediction Using Machine Learning. IEEE Access 9:95608–95621. https://doi.org/10.1109/ACCESS.2021.3093563 10.1109/access.2021.3093563

[9]

Alhothali A, Albsisi M, Assalahi H, Aldosemani T (2022) Predicting student outcomes in online courses using machine learning techniques: A review. Sustainability 14(10):6199 10.3390/su14106199

[10]

Alsariera YA, Baashar Y, Alkawsi G, Mustafa A, Alkahtani AA, Ali N (2022) Assessment and evaluation of different machine learning algorithms for predicting student performance. Comput Intell Neurosci 2022:1–11 10.1155/2022/4151487

[11]

Zeineddine H, Braendle U, Farah A (2021) Enhancing prediction of student success: Automated machine learning approach. Comput Electr Eng 89:106903 10.1016/j.compeleceng.2020.106903

[12]

Hussain S, Khan MQ (2023) Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning. Ann Data Sci 10(3):637–655. https://doi.org/10.1007/s40745-021-00341-0 10.1007/s40745-021-00341-0

[13]

Saifuzzaman M, Parvin M, Jahan I, Moon NN, Nur FN, Shetu SF (2021) “Machine learning approach to predict SGPA and CGPA”. in 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), IEEE, 211–216. 10.1109/icaicst53116.2021.9497847

[14]

Hamim T, Benabbou F, Sael N (2021) Survey of machine learning techniques for student profile modeling. Int J Emerg Technol Learn IJET 16(4):136–151 10.3991/ijet.v16i04.18643

[15]

Bernius JP, Krusche S, Bruegge B (2022) Machine learning based feedback on textual student answers in large courses. Comput Educ Artif Intell 3:100081 10.1016/j.caeai.2022.100081

[16]

Hussain AA, Dimililer K (2021) “Student Grade Prediction Using Machine Learning in Iot Era,” in Forthcoming Networks and Sustainability in the IoT Era, vol. 353, E. Ever and F. Al-Turjman, Eds., in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 353. , Cham: Springer International Publishing, 65–81. https://doi.org/10.1007/978-3-030-69431-9_6. 10.1007/978-3-030-69431-9_6

[17]

Tarik A, Aissa H, Yousef F (2021) Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Comput Sci 184:835–840 10.1016/j.procs.2021.03.104

[18]

Ahajjam T, Moutaib M, Aissa H, Azrour M, Farhaoui Y, Fattah M (2022) Predicting students’ final performance using artificial neural networks. Big Data Min Anal 5(4):294–301 10.26599/bdma.2021.9020030

[19]

Issaro S, Wannapiroon P (2023) Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment. Int J Emerg Technol Learn Online 18(4):66 10.3991/ijet.v18i04.32583

[20]

Priya PM (2023) Prediction system for student’s academic performance to increase university admission system and cumulative grade point average credits. Available: https://jst.org.in/admin/uploads/JST070502.pdf. Accessed 7 Oct 2023 10.46243/jst.2022.v7.i05.pp20-31

[21]

Hoti AH, Zenuni X, Hamiti M, Ajdari J (2023) Student performance prediction using AI and ML: state of the art. In: 2023 12th Mediterranean Conference on Embedded Computing (MECO). IEEE, pp 1–6. Available: https://ieeexplore.ieee.org/abstract/document/10154933/. Accessed 7 Oct 2023 10.1109/meco58584.2023.10154933

[22]

Bujang SDA et al (2022) Imbalanced classification methods for student grade prediction: a systematic literature review. IEEE Access. Available: https://ieeexplore.ieee.org/abstract/document/9965398/. Accessed 7 Oct 2023

[23]

Perez JG, Perez ES (2021) Predicting student program completion using Naïve Bayes classification algorithm. Int J Mod Educ Comput Sci 13(3):57–67 10.5815/ijmecs.2021.03.05

[24]

Haque A (2021) EC-GAN: Low-sample classification using semi-supervised algorithms and GANs (Student Abstract). In: Proceedings of the AAAI conference on artificial intelligence. AAAI, pp 15797–15798. Available: https://ojs.aaai.org/index.php/AAAI/article/view/17895. Accessed 7 Oct 2023 10.1609/aaai.v35i18.17895

[25]

Asselman A, Khaldi M, Aammou S (2023) Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interact Learn Environ 31(6):3360–3379. https://doi.org/10.1080/10494820.2021.1928235 10.1080/10494820.2021.1928235

[26]

Educational data mining: prediction of students' academic performance using machine learning algorithms

Mustafa Yağcı

Smart Learning Environments 2022 10.1186/s40561-022-00192-z

[27]

Ajibade S-SM, Dayupay J, Ngo-Hoang D-L, Oyebode OJ, Sasan JM (2022) Utilization of Ensemble Techniques for Prediction of the Academic Performance of Students. J Optoelectron Laser 41(6):48–54

[28]

Dianah S, Selamat A, Krejcar O (2022) Improve imbalanced multiclass classification based on modified SMOTE and feature selection for student grade prediction. In: Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning. IGI Global, pp 371–389. Available: https://www.igi-global.com/chapter/improve-imbalanced-multiclass-classification-based-on-modified-smote-and-feature-selection-for-student-grade-prediction/296811. Accessed 7 Oct 2023 10.4018/978-1-7998-8686-0.ch014

[29]

Sun Y, Li Z, Li X, Zhang J (2021) Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction. Appl Artif Intell 35(4):290–303. https://doi.org/10.1080/08839514.2021.1877481 10.1080/08839514.2021.1877481

[30]

Jayasundara S, Indika A, Herath D (2022) Interpretable student performance prediction using explainable boosting machine for multi-class classification. In: 2022 2nd International Conference on Advanced Research in Computing (ICARC). IEEE, pp 391–396. Available: https://ieeexplore.ieee.org/abstract/document/9753867/. Accessed 7 Oct 2023 10.1109/icarc54489.2022.9753867

[31]

Hyperparameters and tuning strategies for random forest

Philipp Probst, Marvin N. Wright, Anne‐Laure Boulesteix

WIREs Data Mining and Knowledge Discovery 2019 10.1002/widm.1301

[32]

Rahman M, Islam D, Mukti RJ, Saha I (2020) A deep learning approach based on convolutional LSTM for detecting diabetes. Comput Biol Chem 88:107329 10.1016/j.compbiolchem.2020.107329

[33]

Ansarullah SI, Mohsin Saif S, Abdul Basit Andrabi S, Kumhar SH, Kirmani MM, Kumar DP (2022) An intelligent and reliable hyperparameter optimization machine learning model for early heart disease assessment using imperative risk attributes. J Healthc Eng 2022. Available: https://www.hindawi.com/journals/jhe/2022/9882288/. Accessed 15 Oct 2023 10.1155/2022/9882288

Cited By

112

A machine learning framework for early intervention in e-learning environments

Brahim Jabir, Radoine Hamzaoui · 2025

EDPACS

Machine Learning for Early Detection of Phishing URLs in Parked Domains: An Approach Applied to a Financial Institution

Jaqueline D. Duarte, Pedro Chagas Junior · 2025

IEEE Access

Metrics

112

Citations

33

References

Details

Published: Feb 15, 2024
Vol/Issue: 83(30)
Pages: 74349-74364
License: View

Authors

From the Departments of Infectious Diseases (G.M.V., D.D., D.K., R.G.N., A.H.P., A. Manesh), Medicine (K.G., N.N., A.Z., R.I., S.G.H., S.S., T.D.S., O.C.A.), Pharmacology (B.W.A., B.S.M., A. Mathew), Emergency Medicine (K.P.P.A.), Microbiology (J.A.J.P.), and Critical Care (J.V.P.), Christian Medical College, Vellore, the Department of Medicine, Jawaharlal Institute of Post Graduate Medical Education and Research, Pondicherry (M.W.), the Departments of Internal Medicine (N.S.) and Microbiology (M.B.), Post Graduate Institute of Medical Education and Research, Chandigarh, the Department of Pulmonary Care and Critical Medicine, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak (D.C.), the Department of Medicine, Indira Gandhi Medical College, Shimla (S.K.M.), the Department of Infectious Diseases, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal (K.S.), and the Department of Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati (A. Moh...

A

Abeer Alsadoon

School of Computing and Mathematics Charles Sturt University (CSU) Sydney Campus Wagga Wagga Australia; School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia; School of Information Technology Southern Cross University (SCU) Sydney Australia; Asia Pacific International College (APIC) Information Technology Department Sydney Australia; Kent Institute Australia Sydney Australia

Cite This Article

Yagyanath Rimal, Navneet Sharma, Abeer Alsadoon (2024). The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms. Multimedia Tools and Applications, 83(30), 74349-74364. https://doi.org/10.1007/s11042-024-18426-2

The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

You May Also Like