Application of methods of metric classification for building tools of current and predicted effectiveness evaluation personnel selection

Abstract

to investigate the possibility of using data mining methods for training tools (information systems and technologies) for the formation of current and predictive assessments of the effectiveness of staff recruitment by enterprises and employment agencies. Discussion: for companies that implement independently, without intermediaries, the selection and current assessment of the performance of staff, as well as for recruitment agencies specializing in the selection of personnel in the labor market, modern tools are available that can assess the success and effectiveness of specific job seekers in a particular position in the company or in a certain sphere in the labor market. The creation of such tools should be based on the analysis of retrospective information, which allows to find the laws that determine the success and effectiveness of people who have certain competencies in a certain field in the labor market or in a particular company. Modern research shows that methods of machine learning and data mining are effective technologies for revealing hidden regularities in retrospective data. Results: on the example of recognizing the success of marketers in various areas of activity in the labor market, the possibility of applying metric classification methods in the tasks of predicting the effectiveness of recruitment was explored. Algorithmic and software, developed on the basis of the training of the analyzed methods based on data collected in Burmakova Yu.A. "Individual-personal prerequisites for the professional development of specialists in the advertising business."