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Comparison of machine learning methods for predicting employee absences

Employee absences cannot be avoided but excessive and uncontrolled absences affect not only the companies and employees but also impact the economy, government and society. Though actual losses are hard to compute, absenteeism has been estimated to cost billions in direct and indirect costs. Address...

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Hauptverfasser: Jayme, Alejandra (VerfasserIn) , Lösel, Philipp (VerfasserIn) , Fischer, Joachim E. (VerfasserIn) , Heuveline, Vincent (VerfasserIn)
Dokumenttyp: Buch/Monographie
Sprache:Englisch
Veröffentlicht: Heidelberg Universiätsbibliothek May 3, 2021
Schriftenreihe:Preprint series of the Engineering Mathematics and Computing Lab (EMCL) Preprint no. 2021-02
In: Preprint series of the Engineering Mathematics and Computing Lab (EMCL) (Preprint no. 2021-02)

DOI:10.11588/emclpp.2021.02.81078
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.11588/emclpp.2021.02.81078
Verlag, kostenfrei, Volltext: https://journals.ub.uni-heidelberg.de/index.php/emcl-pp/article/view/81078
Volltext
Verfasserangaben:Alejandra Jayme, Philipp D. Lösel, Joachim Fischer, Vincent Heuveline

MARC

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