A Dashboard for Simplifying Machine Learning Models using Feature Importances and Spurious Correlation Analysis
| dc.contributor.author | Cech, Tim | en_US |
| dc.contributor.author | Kohlros, Erik | en_US |
| dc.contributor.author | Scheibel, Willy | en_US |
| dc.contributor.author | Döllner, Jürgen | en_US |
| dc.contributor.editor | Kucher, Kostiantyn | en_US |
| dc.contributor.editor | Diehl, Alexandra | en_US |
| dc.contributor.editor | Gillmann, Christina | en_US |
| dc.date.accessioned | 2024-05-21T08:44:47Z | |
| dc.date.available | 2024-05-21T08:44:47Z | |
| dc.date.issued | 2024 | |
| dc.description.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. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | EuroVis 2024 - Posters | |
| dc.identifier.doi | 10.2312/evp.20241075 | |
| dc.identifier.isbn | 978-3-03868-258-5 | |
| dc.identifier.pages | 3 pages | |
| dc.identifier.uri | https://doi.org/10.2312/evp.20241075 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/evp20241075 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Human-centered computing → Visualization techniques; Information systems → Users and interactive retrieval | |
| dc.subject | Human centered computing → Visualization techniques | |
| dc.subject | Information systems → Users and interactive retrieval | |
| dc.title | A Dashboard for Simplifying Machine Learning Models using Feature Importances and Spurious Correlation Analysis | en_US |