Year: 2024
Type: article
Abstract: The need to increase employee performance and productivity has become vital in most companies nowadays, considering the number of changes that processes and people have faced during recent years in many organizations. This becomes even more important as it can sustain the growth of the company, as well as the competitiveness. This work will present multiple methods and comparisons between them for the process of building a machine learning algorithm to predict performance scores for employees in one organization; these methods include pre-processing the data, selecting the best variables, building the best algorithms for the available data, and tuning their hyperparameters. The current research aims to conclude on a collection of practices that will determine the best predictions for the given variables, so that human opinion can become less influential in employee appraisal, increasing objectivity and overall productivity.
Source: Applied Sciences
DOI: 10.3390/app14083254
Institution: Bucharest University of Economic Studies
Language: English
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4394768415