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KMID : 1155220200450010016
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Journal of the Korean Society of Health Information and Health Statistics
2020 Volume.45 No. 1 p.16 ~ p.29
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Visualization with Various Tree-based Regression Results in Health Information Data
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Cho Hyun-Sun
Lee Eun-Kyung
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Abstract
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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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KEYWORD
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Regression analysis, Tree-based regression model, Bagging, Boosting, Data visualization
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