IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Study on Prediction Performance Measurement of Automated Machine Learning: Focusing on WiseProphet, a Korean Auto ML Service

A Study on Prediction Performance Measurement of Automated Machine Learning: Focusing on WiseProphet, a Korean Auto ML Service
View Sample PDF
Author(s): Euntack Im (Soongsil University, South Korea), Jina Lee (Soongsil University, South Korea), Sungbyeong An (Soongsil University, South Korea)and Gwangyong Gim (Soongsil University, South Korea)
Copyright: 2023
Volume: 11
Issue: 1
Pages: 11
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.315656

Purchase


Abstract

In digital economics, where value creation using big data becomes important, the ability to analyze data using machine learning and deep learning technology is a key activity in corporate activities. Nevertheless, companies consider it difficult to introduce machine learning and artificial intelligence technologies because they need an understanding of the business as well as data and analysis algorithms. Accordingly, services such as automated machine learning have emerged for easy use of machine learning. In this study, the authors explored the automated machine learning service and compared the random forest and extreme gradient boosting analysis results using WiseProphet and Python. WiseProphet is used as a representative of automated machine learning solutions because it is a cloud-based service that anyone can easily access and can be used in various ways. It is contrasted with the model implemented by Python, which writes code with No coding. As a result of comparing the prediction performance, WiseProphet automatically outperformed the analysis result by parameter optimization.

Related Content

. © 2024.
Yogesh M. Kamble, Raj B. Kulkarni. © 2024. 10 pages.
Zachary Estreito, Vinh Le, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 15 pages.
Chase D. Carthen, Araam Zaremehrjardi, Vinh Le, Carlos Cardillo, Scotty Strachan, Alireza Tavakkoli, Frederick C. Harris Jr., Sergiu M. Dascalu. © 2024. 14 pages.
Partha Ghosh, Takaaki Goto, Leena Jana Ghosh, Giridhar Maji, Soumya Sen. © 2024. 15 pages.
Megha Bhushan, Utkarsh Verma, Chetna Garg, Arun Negi. © 2024. 14 pages.
Deepkiran Munjal, Laxman Singh, Mrinal Pandey, Sachin Lakra. © 2023. 25 pages.
Body Bottom