THE SIGNIFICANCE AND APPLICATION OF COMPACTNESS MEASURES IN MACHINE LEARNING
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This article explores the concept of compactness as one of the factors that contribute to the generalization ability of models in machine learning. Compactness is a characteristic that reflects the closeness, density, and organization of data, significantly impacting a model's performance on test data. The article provides both an intuitive and formal description of compactness measures, explains how they can be evaluated, and in which scenarios their use is justified. It also discusses the relationship between generalization ability and compactness using practical examples. The results of the study open new possibilities for improving model quality through compactness assessment.
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