Visualizing Prediction Provenance in Regression Random Forests

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
} }
Citation