Interpretable Machine Learning: Explainability in Algorithm Design

Xueyi Cheng; Chang Che

doi:10.70393/6a69656173.323337

OPEN ACCESS |Research Article ||1 December 2024

Interpretable Machine Learning: Explainability in Algorithm Design

Xueyi Cheng

Duke University

Frances.cheng17@gmail.com

Researcher at Duke University.

Chang Che

The George Washington University

cche57@gwmail.gwu.edu

The George Washington University, US.

* Corresponding Author¹: Xueyi Cheng, E-Mail: Frances.cheng17@gmail.com

Publication

Accepted Unknow ; Published 2024 December 1

Journal of Industrial Engineering and Applied Science, 2024, 2(6), 3005-6071.

Abstract

In recent years, there is a high demand for transparency and accountability in machine learning models, especially in domains such as healthcare, finance and etc. In this paper, we delve into deep how to make machine learning models more interpretable, with focus on the importance of the explainability of the algorithm design. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML. To that end, we first introduce the AutoML technology and review its various tools and techniques.

Keywords

Machine Learning , AutoML , Computational Efficiency .

Metadata

DOI:

10.70393/6a69656173.323337

ARK:

ark:/40704/JIEAS.v2n6a07

Pages: 65-70

References: 22

Disciplines: Computer Science

Subjects: Machine Learning

Cite This Article

APA Style

Cheng, X. & Che, C. (2024). Interpretable machine learning: explainability in algorithm design. Journal of Industrial Engineering and Applied Science, 2(6), 65-70. https://doi.org/10.70393/6a69656173.323337

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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