A Dashboard for Simplifying Machine Learning Models using Feature Importances and Spurious Correlation Analysis
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Machine Learning models underlie a trade-off between accurracy and explainability. Given a trained, complex model, we contribute a dashboard that supports the process to derive more explainable models, here: Fast-and-Frugal Trees, with further introspection using feature importances and spurious correlation analyses. The dashboard further allows to iterate over the feature selection and assess the trees' performance in comparison to the complex model.
Description
CCS Concepts: Human-centered computing → Visualization techniques; Information systems → Users and interactive retrieval
@inproceedings{10.2312:evp.20241075,
booktitle = {EuroVis 2024 - Posters},
editor = {Kucher, Kostiantyn and Diehl, Alexandra and Gillmann, Christina},
title = {{A Dashboard for Simplifying Machine Learning Models using Feature Importances and Spurious Correlation Analysis}},
author = {Cech, Tim and Kohlros, Erik and Scheibel, Willy and Döllner, Jürgen},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-258-5},
DOI = {10.2312/evp.20241075}
}