COMPARISON OF THE EFFICIENCY OF VARIOUS MACHINE LEARNING ALGORITHMS IN MULTI-CLASS TEXT CLASSIFICATION
- Authors
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Muhamediyeva D. T.
National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Namangan State University
Author
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Mamatov A. A.
National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Namangan State University
Author
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- Keywords:
- Text classification, TF-IDF, machine learning, NLP, Logistic Regression, Support Vector Machine, Naive Bayes, Random Forest, model efficiency, algorithm comparison.
- Abstract
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This article considers the issue of automatic classification of text data. In the study, documents were transferred to a numerical character space using TF-IDF vectorization technology based on Machine Learning and Natural Language Processing methods. The accuracy, training and testing time of Logistic Regression, Ridge Classifier, k-Nearest Neighbors, Random Forest, Linear Support Vector Machine, Stochastic Gradient Descent, Nearest Centroid and Complement Naive Bayes algorithms in the classification process were analyzed. The experiments were carried out on four categories of 20 Newsgroups text sets. The results showed that there are significant differences in accuracy and computational speed between the algorithms and proved the importance of choosing an optimal model for real-time text analysis systems.
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- Published
- 2026-03-24
- Issue
- Vol. 2 No. 3 (2026)
- Section
- Articles
- License
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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