EnsembleForge: A Comprehensive Framework for Simplified Training and Deployment of Stacked Ensemble Models in Classification Tasks

  • Ubong David Essien Akwa Ibom State University, Nigeria
  • Godwin Okon Ansa Akwa Ibom State University, Nigeria
  • Aloysius Akpnobong Akwa Ibom State University, Nigeria
Keywords: Machine Learning, Stacking Technique, Ensemble Model, Classification


In this work, we introduce EnsembleForge, a versatile framework designed to streamline machine learning experimentation and simplify classification tasks. Leveraging the stacking ensemble method, EnsembleForge offers an intuitive platform built upon the Scikit-learn library. This framework facilitates seamless model implementation and evaluation, supporting both Randomized and Grid Search for hyperparameter optimization. Our experiments with publicly available datasets demonstrate the ease of use and effectiveness of EnsembleForge in experimenting with various algorithms. With its adaptability and innovation, EnsembleForge showcases promising potential to serve as an asset for researchers and practitioners seeking to achieve optimal model performance in their machine learning endeavors.


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Author Biography

Godwin Okon Ansa, Akwa Ibom State University

Associate Professor
Department Of Computer Science,Akwa Ibom State University


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How to Cite
Essien, U., Ansa, G., & Akpnobong, A. (2024). EnsembleForge: A Comprehensive Framework for Simplified Training and Deployment of Stacked Ensemble Models in Classification Tasks. Journal of Information Systems and Informatics, 6(1), 68-82. https://doi.org/10.51519/journalisi.v6i1.643