Statistical Verification of Linear Classifiers

• URL: http://arxiv.org/abs/2501.14430v1
• Date: Fri, 24 Jan 2025 11:56:45 GMT
• Title: Statistical Verification of Linear Classifiers
• Authors: Anton Zhiyanov, Alexander Shklyaev, Alexey Galatenko, Vladimir Galatenko, Alexander Tonevitsky,
• Abstract summary: We propose a homogeneity test closely related to the concept of linear separability between two samples. We focus on establishing upper bounds for the test's emphp-value when applied to two-dimensional samples.
• Score: 76.95660509846216
• License:
• Abstract: 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.

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