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Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy

Author

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  • Lívia Kelebercová

    (Department of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, Trieda Andreja Hlinku 1, 949 74 Nitra, Slovakia)

  • Michal Munk

    (Department of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, Trieda Andreja Hlinku 1, 949 74 Nitra, Slovakia)

  • František Forgáč

    (Department of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, Trieda Andreja Hlinku 1, 949 74 Nitra, Slovakia)

Abstract

The need to train experts who will be able to apply machine learning methods for knowledge discovery is increasing. Building an effective machine learning model requires understanding the principle of operation of the individual methods and their requirements in terms of data pre-preparation, and it is also important to be able to interpret the acquired knowledge. This article presents an experiment comparing the opinion of the 42 students of the course called Introduction to Machine Learning on the complexity of the method, preprocessing, and interpretability of symbolic, subsymbolic and statistical methods with the correctness of individual methods expressed on the classification task. The methodology of the implemented experiment consists of the application of various techniques in order to search for optimal models, the accuracy of which is subsequently compared with the results of a knowledge test on machine learning methods and students’ opinions on their complexity. Based on the performed non-parametric and parametric statistic tests, the null hypothesis, which claims that there is no statistically significant difference in the evaluation of individual methods in terms of their complexity/demandingness, the complexity of data preprocessing, the comprehensibility of the acquired knowledge and the correctness of the classification, is rejected.

Suggested Citation

  • Lívia Kelebercová & Michal Munk & František Forgáč, 2023. "Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2922-:d:1182789
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    References listed on IDEAS

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    1. Dalibor Gonda & Gabriela Pavlovičová & Viliam Ďuriš & Anna Tirpáková, 2022. "Implementation of Pedagogical Research into Statistical Courses to Develop Students’ Statistical Literacy," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    2. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.
    3. Mabrouka Shahat Younis & Elfargani, 2022. "The Benefits Of Artificial Intelligence In Construction Projects," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 6(2), pages 47-51, June.
    4. Dalibor Gonda & Viliam Ďuriš & Anna Tirpáková & Gabriela Pavlovičová, 2022. "Teaching Algorithms to Develop the Algorithmic Thinking of Informatics Students," Mathematics, MDPI, vol. 10(20), pages 1-13, October.
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