Visualizing Prediction Provenance in Regression Random Forests
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Random forest models are widely used in many application domains due to their performance and the fact that their constituent decision trees carry clear decision rules. Yet, the provenance of the predictions made by an entire forest is complex to grasp, which motivates application domain experts to adopt black-box testing strategies. We propose in this paper a coordinated multiple view system allowing to shed more light on prediction provenance, uncertainty and error in terms of bias and variance at the global model scale or at the local scale of decision paths and individual instances.
Description
CCS Concepts: Human-centered computing --> Visualization; Computing methodologies --> Classification and regression trees
@inproceedings{10.2312:evp.20221124,
booktitle = {EuroVis 2022 - Posters},
editor = {Krone, Michael and Lenti, Simone and Schmidt, Johanna},
title = {{Visualizing Prediction Provenance in Regression Random Forests}},
author = {Médoc, Nicolas and Ciorna, Vasile and Petry, Frank and Ghoniem, Mohammad},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {10.2312/evp.20221124}
}