Geodesic
Building a machine learning model
News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 2, pp. 11-22.

Voir la notice de l'article provenant de la source Math-Net.Ru

The article presents development of a machine learning model for predicting fraudulent transactions using transactional data from a bank. It discusses the features of encoding categorical variables related to the presence of time in the transactional data to avoid information leakage. Additionally, experiments were conducted on the application of bagging and the creation of additional variables based on their contribution to the final prediction using Shapley values. The quality metrics of the machine learning model are examined and analyzed.
Mots-clés : fraudulent transactions
Keywords: catboost, encoding categorical variables, catboost_encoder, target_encoder, bagging, variables creation, Shapley values 
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A. F. Konstantinov; L. P. Dyakonova. Building a machine learning model. News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 2, pp. 11-22. http://geodesic.mathdoc.fr/item/IZKAB_2025_27_2_a0/

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