보건정보 자료를 이용한 나무구조회귀모형의 시각화에 대한 고찰
Visualization with Various Tree-based Regression Results in Health Information Data
- 주제(키워드) . , Regression analysis , Tree-based regression model , Bagging , Boosting , Data visualization
- 주제(기타) 보건통계
- 설명문(URI) https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002565475
- 등재 KCI등재
- 발행기관 한국보건정보통계학회
- 발행년도 2020
- URI http://www.dcollection.net/handler/ewha/000000175927
- 본문언어 한국어
초록/요약
Objectives: Among machine learning techniques, a tree-based regression model is widely used as easy to interpret and easy to use results. Methods: In this study, we examine the characteristics of regression models using various tree structures implemented in R, and apply them to public health data for visual representation and analysis to enhance understanding of the models and data. Results: We also look at the random forest, gradient boosting model, and xgboost that incorporate the results of various tree-structured model estimates using bagging and boosting, and visualize these estimation processes to compare the results of the model estimates. It also compares the performance of various tree-structured models using various public health data. It visually examines that the performance of the tree-structured models may vary depending on the characteristics of the data. Conclusions: Through this study, we apply the tree-structured models to public health data to enhance understanding of the data and to help identify and visualize the mechanism of the data.
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