Comparative Analysis of Machine Learning Models for Employee Performance Evaluation

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@article{1707869,
      author = {Akinsiku Ayokunle Michael, Akintola K. G.},
      title = {Comparative Analysis of Machine Learning Models for Employee Performance Evaluation},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {535-543},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707869.pdf},
      abstract = {Evaluating employees? performance is an important tool used by management of organizations to make decisions related to employee growth, promotions, compensation or renumeration, training, and appraise organizational growth. In this research, machine learning techniques have been applied to staff performance evaluation.   The study encompassed the development and evaluation of multiple predictive models, each harnessed to uncover patterns and access the performance of staff. Comparative analysis of selected algorithms, including Naive Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, Neural Network and Ensemble model were carried out to determine which gives the best result. These models were trained and rigorously tested to ascertain their efficacy in predicting staff performance. The outcomes of the study provide valuable insights into the potential of machine learning approaches to unravel staff performance. },
      keywords = {Human resources, Machine Learning Model, Performance Evaluation, Ensemble},
      month = {April},
  }