СОЗДАНИЕ ПРОГНОЗНЫХ ТЕХНОЛОГИЙ ОЦЕНКИ ЭФФЕКТИВНОСТИ ПОДБОРА ПЕРСОНАЛА НА ОСНОВЕ МЕТОДА ДЕРЕВЬЕВ КЛАССИФИКАЦИИ

Abstract

This article analyzes the application of the method of classification trees in problems of formation in the process of recruiting the staff projections of success and effectiveness of the respondents in a specific position in a company or in total in a certain kind of activity on the the labour market. Classification trees is one of the modern, but at the same time, good proven methods of machine learning, which allows on the basis of retrospective information stored in databases, reveal hidden patterns and build rules of thumb to predict the conditioning object to a result class. The experiments carried out in the framework of the studies show that the tasks of recruiting staff, subject to the availability of retrospective database containing characteristics of respondents, formed on the basis of competence and individually-personal testing method classification trees shows a fairly high recognition accuracy of success respondents in one form or another activity. Proposed algorithmic and software developed on the basis of the learning method of classification trees for data collected in the study J. A. Burmakova «Individually-personal background of professional development of specialists in advertising».