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Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms.

Fahad Kamal Alsheref1, Ibrahim Eldesouky Fattoh2, Waleed M Ead1

  • 1Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.

Computational Intelligence and Neuroscience
|July 7, 2022
PubMed
Summary
This summary is machine-generated.

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Employee attrition is a significant challenge for companies. This research introduces an automated model using predictive analytics and hyperparameter autotuning to optimize employee retention strategies and identify the best predictive model.

Area of Science:

  • Business Analytics
  • Human Resources Management
  • Machine Learning

Background:

  • Employee attrition poses a significant financial and operational challenge for organizations due to the high cost of replacing experienced personnel.
  • Retaining competent and experienced employees is crucial for company success and sustainability.
  • Existing methods for predicting employee attrition may not be universally optimal across different business contexts.

Purpose of the Study:

  • To develop and evaluate an automated model for predicting employee attrition.
  • To identify the most effective predictive analytical techniques and pipeline architectures for employee attrition prediction.
  • To implement an autotuning approach for optimizing model hyperparameters and create an ensemble model for superior performance.

Main Methods:

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  • Application of various predictive analytical techniques with diverse pipeline architectures.
  • Implementation of an autotuning approach to determine optimal hyperparameter combinations.
  • Development of an ensemble model to select the most efficient predictive model based on assessment measures.

Main Results:

  • The study demonstrated that no single model is universally ideal for all business contexts.
  • The proposed ensemble model achieved optimal performance according to the defined requirements.
  • The automated model effectively predicted employee attrition, offering a valuable tool for businesses.

Conclusions:

  • Automated predictive models, particularly ensemble approaches, can significantly aid in addressing employee attrition.
  • Hyperparameter autotuning is essential for maximizing the efficiency of predictive models in HR analytics.
  • While no model is perfect for every scenario, the developed approach provides a robust and adaptable solution for employee retention challenges.