Finding Multiple Interpretations in Datasets
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Computer Science > Machine Learning
Title:Finding Multiple Interpretations in Datasets
Abstract:In this paper, we propose an approach to finding sets of similar-performing models (in terms of loss/accuracy measurements) with highly different context-aware characteristics. Through experiments on the METABRIC dataset, we show that the proposed method finds multiple models with highly different gene expressions than those found by the control methodology without performance penalties. We argue that the proposed methodology is important whenever one aims to analyze any global characteristic of a model to extract insight into the underlying phenomenon being studied.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.12277 [cs.LG] |
| (or arXiv:2606.12277v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12277
arXiv-issued DOI via DataCite (pending registration)
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