Журнал «Современная Наука»

Russian (CIS)English (United Kingdom)
 MOSCOW +7(495)-142-86-81

COMPARISON OF BINARY CLASSIFICATION METRICS FOR EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS

Shirokova Ekaterina Vasilyevna  (Ph.D. of Physico-mathematical Sciences, Associate Professor Federal State Budgetary Educational Institution of Higher Education «Bauman Moscow State Technical University» (Kaluga Branch) )

The article compares various binary classification metrics to evaluate the effectiveness of machine learning algorithms. Various approaches are considered, such as the support vector machine method, the decision tree method, the random forest method, and the gradient boosting method. The advantages and disadvantages of metrics in the case of balanced and unbalanced data are analyzed, as well as examples of their implementation using the Python Scikit-learn library.

Keywords:machine learning, data classification, class imbalance.
 

Read the full article …

Citation link:
Shirokova E. V. COMPARISON OF BINARY CLASSIFICATION METRICS FOR EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2025. -№02. -С. 164-169 DOI 10.37882/2223-2966.2025.02.40
LEGAL INFORMATION:
Reproduction of materials is permitted only for non-commercial purposes with reference to the original publication. Protected by the laws of the Russian Federation. Any violations of the law are prosecuted.
© ООО "Научные технологии"

Terms Publications

Issues of the journals (Archive)

ECONOMICS AND LAW
HUMANITIES
NATURAL AND TECHNICAL SCIENCES
COGNITION

Серия - Natural and Technical Sciences