Interpretable Visualizations of Data Spaces for Classification Problems
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917339419639808 |
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| author | Jorgensen, Christian Lin, Arthur Y. Vasavada, Rhushil Cersonsky, Rose K. |
| author_facet | Jorgensen, Christian Lin, Arthur Y. Vasavada, Rhushil Cersonsky, Rose K. |
| contents | How do classification models "see" our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes; however, our current set of visualization techniques makes this prospect difficult. In this work, we propose a hybrid supervised-unsupervised technique distinctly suited to visualizing the decision boundaries determined by classification problems. This method provides a human-interpretable map that can be analyzed qualitatively and quantitatively, which we demonstrate through visualizing and interpreting a decision boundary for chemical neurotoxicity. While we discuss this method in the context of chemistry-driven problems, its application can be generalized across subfields for "unboxing" the operations of machine-learning classification models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Visualizations of Data Spaces for Classification Problems Jorgensen, Christian Lin, Arthur Y. Vasavada, Rhushil Cersonsky, Rose K. Machine Learning N/A How do classification models "see" our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes; however, our current set of visualization techniques makes this prospect difficult. In this work, we propose a hybrid supervised-unsupervised technique distinctly suited to visualizing the decision boundaries determined by classification problems. This method provides a human-interpretable map that can be analyzed qualitatively and quantitatively, which we demonstrate through visualizing and interpreting a decision boundary for chemical neurotoxicity. While we discuss this method in the context of chemistry-driven problems, its application can be generalized across subfields for "unboxing" the operations of machine-learning classification models. |
| title | Interpretable Visualizations of Data Spaces for Classification Problems |
| topic | Machine Learning N/A |
| url | https://arxiv.org/abs/2503.05861 |