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Analysis of the Effect of Step-by-step Elements of Machine Learning Model on the Performance of AI Model using German Credit Risk Data
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German Credit Risk 데이터를 사용한 머신러닝 모델의 단계별 요소들이 AI 모델의 성능에 미치는 영향 분석

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Type
Academic journal
Author
Pill-Won Park (토탈시스)
Journal
Korean Institute of Information Technology The Journal of Korean Institute of Information Technology Vol.20 No.11 KCI Accredited Journals
Published
2022.11
Pages
1 - 7 (7page)
DOI
10.14801/jkiit.2022.20.11.01

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Analysis of the Effect of Step-by-step Elements of Machine Learning Model on the Performance of AI Model using German Credit Risk Data
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Machine learning is used in various fields, and various algorithms have been developed according to the type and purpose of data. The performance of the machine learning model is affected by the step-by-step setting even if the same algorithm is used, and research on this is needed. However, studies on the effects of specific procedures or specific parameters on the model have been conducted, but studies that comprehensively analyze them have been insufficient. In this paper, after summarizing the processing steps required to develop the machine learning model, the effect of each step on the performance of the machine learning model was analyzed. Processing steps were divided into steps of data purification, algorithm selection, hyper-parameter adjustment, and verification ratio adjustment, which were measured using Kaggle"s German credit risk data and machine learning automation tools.
#machine learning #performance of AI model #step of machine learning #performance analysis #auto ML

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 머신러닝 프로세스의 단계별 성능 연관 요소 분석
Ⅲ. 실험 및 고찰
Ⅳ. 결론 및 향후 과제
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