Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Original Research

Comparative Analysis of Supervised Learning Algorithms

  1. Kalpesh U. Gundigara Department of Computer Science, Shri Swaminarayan College of Computer Science, Bhavnagar
  2. Jeet S. Pandya Department of Computer Science, Shri Swaminarayan College of Computer Science, Bhavnagar

Abstract

Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains such as image and speech recognition, natural language processing, fraud detection in financial systems, spam filtering, recommendation systems, and medical diagnosis, where reliable and interpretable predictions are essential This study provides an in-depth comparative evaluation of widely adopted supervised learning techniques, including logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors (KNN), and random forests. Each of these algorithms has distinct characteristics, strengths, and limitations in terms of performance, interpretability, scalability, and computational complexity. To achieve an objective and thorough comparison, the algorithms are assessed using standard evaluation measures, including accuracy, precision, recall, F1-score, and training duration. These metrics help assess not only the correctness of predictions but also the efficiency and robustness of the models under different conditions. For experimental validation, a dataset consisting of 65 one-day international cricket match records was collected and analyzed. The dataset includes relevant features that influence match outcomes, enabling effective supervised learning-based classification. By applying and evaluating the selected algorithms on this real-world dataset, the study highlights how algorithm performance varies depending on data characteristics and problem context. The results of this comparative study aim to provide practical guidance to researchers, data scientists, and practitioners in selecting the most suitable supervised learning algorithm for their specific application requirements and constraints.

Keywords

  • Logistic Regression
  • Machine Learning
  • random forest
  • supervised learning
  • support vector machine learning

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