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Interactive graphics for visually diagnosing forest classifiers in R
- Title
- Interactive graphics for visually diagnosing forest classifiers in R
- Authors
- da Silva, Natalia; Cook, Dianne; Lee, Eun-Kyung
- Ewha Authors
- 이은경
- SCOPUS Author ID
- 이은경
- Journal Title
- COMPUTATIONAL STATISTICS
- ISSN
- 0943-4062
1613-9658
- Citation
- COMPUTATIONAL STATISTICS
- Keywords
- Statistical visualization; Interactive visualization; Interpretable machine learning; Ensemble model
- Publisher
- SPRINGER HEIDELBERG
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
Early Access
- Abstract
- This article describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble since it is produced by bagging multiple trees. The process of bagging and combining results from multiple trees produces numerous diagnostics which, with interactive graphics, can provide a lot of insight into class structure in high dimensions. Various aspects of models are explored in this article, to assess model complexity, individual model contributions, variable importance and dimension reduction, and uncertainty in prediction associated with individual observations. The ideas are applied to the random forest algorithm and projection pursuit forest but could be more broadly applied to other bagged ensembles helping in the interpretability deficit of these methods. Interactive graphics are built in R using the ggplot2, plotly, and shiny packages.
- DOI
- 10.1007/s00180-023-01323-x
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
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