UDK 159.9 Психология
GRNTI 12.09 Развитие науки
This article is an overview of the decision tree method and its application in the field of management activities. The decision tree method is a powerful machine learning tool that can be effectively used for making managerial decisions, predicting the results of business processes, identifying key success factors and optimizing strategic processes, as well as reducing personal factors such as the manager’s psychological barriers. The article discusses the basic principles of the method, its application in management analysis, as well as ways to improve the quality of decision tree models. The author, using general scientific and special methods, provides an example of a simple but effective system for using the decision tree method in various areas of management, which makes this article a useful resource for managers and analysts interested in applying modern data analysis methods to improve managerial decisions. In conclusion, findings are drawn about the advisability of using the decision tree method, on the basis of which a scalable management decision-making system can be created using a universal, simple learning algorithm for artificial intelligence technologies and can be implemented in the company’s strategic management.
decision tree, machine learning method in psychology, vertical analysis, management, operation, planning, managerial decisions, strategic analysis
1. Barabanshchikov V.A. Systematic Approach in Structure of Psychological Cognition. Methodology and History of Psychology. 2007;2(1):86-99.
2. Vorobiov A.V. The Review of Mathematical Methods Application in Psychological Researches. Psychological Studies. 2010;2:8.
3. Znakov V.V. Dynamic Approach to the Research of the Personality and the Procedural Analysis in Psychology of the Subject. Psikhologicheskii Zhurnal. 2019;40(5):27-34. DOIhttps://doi.org/10.31857/S020595920006073-6.0.
4. Reznichenko N.S., Shilov S.N., Abdulkin V.V. Neural Network Approach to the Solution of the Medical-Psychological Problems and in Diagnosis Process for Persons With Disabilities (Literature Review). Journal of Siberian Federal University. Series: Humanities. 2013;6(9):1256-1264.
5. Shadrikov V.D. To New Psychological Theory of Abilities and Giftedness. Psikhologicheskii Zhurnal. 2019;40(2):15-26. DOIhttps://doi.org/10.31857/S020595920002981.
6. Adriaens F., Lijffijt J., De Bie T. Subjectively Interesting Connecting Trees and Forests. Data Mining and Knowledge Discovery. 2019;33:1088-1124. DOIhttps://doi.org/10.1007/s10618-019-00627-1.
7. Delibalt V.V., Degtyaryov A.V., Dozortseva E.G., Chirkina R.V., Dvoryanchikov N.V., Pimonov V.A., et al. Evaluation of Cognitive Functions, Personality and Regulatory Sphere in Minors With Deviant and Delinquent Behavioor Within the Authority of the Psychological, Medical and Educational Committee. International Journal of Cognitive Research in Science, Engineering and Education. 2017;5(2):107-118. DOIhttps://doi.org/10.5937/IJCRSEE1702107D.
8. Geary D.C. Efficiency of Mitochondrial Functioning as the Fundamental Biological Mechanism of General Intelligence (g). Psychological Review. 2018;125(6):1028-1050. DOIhttps://doi.org/10.1037/rev0000124.
9. Genrikhov I.E., Djukova E.V. About Methods of Synthesis Complete Regression Decision Trees. Pattern Recognition and Image Analysis. 2019;29:457-470. DOIhttps://doi.org/10.1134/S1054661819030040.
10. Genrikhov I.E., Djukova E.V., Zhuravlev V.I. On Full Regression Decision Trees. Pattern Recognition and Image Analysis. 2017;27:1-7. DOIhttps://doi.org/10.1134/S1054661817010047.
11. Suzin G., Ravona-Springer R, Ash E.L., Davelaar E.J., Usher M. Differences in Semantic Memory Encoding Strategies in Young, Healthy Old and MCI Patients. Frontiers in Aging Neuroscience. 2019;11:306. DOIhttps://doi.org/10.3389/fnagi.2019.00306.
