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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.14430 |
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| _version_ | 1866913664523567104 |
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| author | Zhiyanov, Anton Shklyaev, Alexander Galatenko, Alexey Galatenko, Vladimir Tonevitsky, Alexander |
| author_facet | Zhiyanov, Anton Shklyaev, Alexander Galatenko, Alexey Galatenko, Vladimir Tonevitsky, Alexander |
| contents | We propose a homogeneity test closely related to the concept of linear separability between two samples. Using the test one can answer the question whether a linear classifier is merely ``random'' or effectively captures differences between two classes. We focus on establishing upper bounds for the test's \emph{p}-value when applied to two-dimensional samples. Specifically, for normally distributed samples we experimentally demonstrate that the upper bound is highly accurate. Using this bound, we evaluate classifiers designed to detect ER-positive breast cancer recurrence based on gene pair expression. Our findings confirm significance of IGFBP6 and ELOVL5 genes in this process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical Verification of Linear Classifiers Zhiyanov, Anton Shklyaev, Alexander Galatenko, Alexey Galatenko, Vladimir Tonevitsky, Alexander Machine Learning Probability Statistics Theory Applications 62P10 G.3 We propose a homogeneity test closely related to the concept of linear separability between two samples. Using the test one can answer the question whether a linear classifier is merely ``random'' or effectively captures differences between two classes. We focus on establishing upper bounds for the test's \emph{p}-value when applied to two-dimensional samples. Specifically, for normally distributed samples we experimentally demonstrate that the upper bound is highly accurate. Using this bound, we evaluate classifiers designed to detect ER-positive breast cancer recurrence based on gene pair expression. Our findings confirm significance of IGFBP6 and ELOVL5 genes in this process. |
| title | Statistical Verification of Linear Classifiers |
| topic | Machine Learning Probability Statistics Theory Applications 62P10 G.3 |
| url | https://arxiv.org/abs/2501.14430 |