ALGORITHMS FOR CALCULATING ESTIMATES BASED ON MINIMIZING ERRORS IN OBJECT CLASSIFICATION
Received: 2025-05-20
Published: 2026-10-01
Abstract
In this study, the distribution of real objects and synthetically augmented classes was analyzed,
and their impact on machine learning models was evaluated. The training outcomes of logistic regression, decision trees, random forest, and SVM models using synthetic data were compared with those trained on real object datasets. Experimental results demonstrated that the use of synthetically augmented data improves the classification model's accuracy, with particularly significant enhancements observed in the
performance of certain algorithms.
Keywords
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