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Journal of Visualization
Erschienen in: Journal of Visualization 1/2019

24.09.2018 | Regular Paper

GBRTVis: online analysis of gradient boosting regression tree

verfasst von: Yifei Huang, Yuhua Liu, Chenhui Li, Changbo Wang

Erschienen in: Journal of Visualization | Ausgabe 1/2019

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Abstract

Visualizations of machine learning models have developed rapidly during these days, attracting great interests of industry and researchers. However, a pipeline that visualizations are created from logged data is a time-consuming process. In this work, we adopt progressive visual analytics to propose a new pipeline to facilitate the visual analysis progress of gradient boosting regression tree (GBRT). Visualizations such as tree view, instances view, and cluster view are created according to different types of data in real time. Users can explore GBRT with different visualization components interactively through GBRTVis. Case studies demonstrate that our pipeline can improve the efficiency of the training process and understanding. Furthermore, we propose a mixed structure of GBRT to improve itself. Two tests on different datasets show the effectiveness of the improvement.

Graphical Abstract

Vorheriger Artikel Visualization of technical and tactical characteristics in fencing
Nächster Artikel Visual analysis of traffic data via spatio-temporal graphs and interactive topic modeling

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Metadaten
Titel
GBRTVis: online analysis of gradient boosting regression tree
verfasst von
Yifei Huang
Yuhua Liu
Chenhui Li
Changbo Wang
Publikationsdatum
24.09.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Visualization / Ausgabe 1/2019
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI
https://doi.org/10.1007/s12650-018-0514-2

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