In-Depth Comparison of Supervised Classification Models - Performance and Adaptability to Practical Requirements

In this paper, we carried out an in-depth comparative analysis of five major supervised classification algorithms: Naïve Bayes, Decision Tree, Random Forest, KNN and SVM. These models were evaluated through a rigorous literature review, based on 20 criteria grouped into five key dimensions: algorithm performance, computational efficiency, practicality and ease of use, data compatibility and practical applicability. The results show that each algorithm has specific strengths and limitations: SVM and Random Forest stand out for their robustness and accuracy in complex environments, while Naïve Bayes and Decision Tree are appreciated for their speed, simplicity and interpretability. KNN, despite its intuitive approach, suffers from high complexity in the prediction phase, limiting its effectiveness on large datasets. This study aims to provide a structured framework for researchers and practitioners in various fields, such as healthcare, finance, industry and education, where supervised classification algorithms play a central role in decision-making. In addition, the results highlight the importance of selecting algorithms according to specific needs, and open up promising prospects, including the development of hybrid models and improved real-time data processing.

@article{KHALDI2025,
  title     = {In-Depth Comparison of Supervised Classification Models - Performance and Adaptability to Practical Requirements},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mouataz IDRISSI KHALDI and Allae ERRAISSI and Mustapha HAIN and Mouad BANANE},
  doi       = {10.14569/IJACSA.2025.0160862},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160862}
}