Algorithmic Showdown: Analysing the Performance of KNN, Random Forest, and SVM across Varied Dimensions - Ramalakshmi, S.; Asha, G. (Indian Journal of Computer Science, 2025)
Data classification plays an essential role in machine learning and data analysis by grouping data points into predetermined categories based on their attributes. The goal is to develop models that can reliably determine the category of new, previously unseen data. This process is integral to many fields such as healthcare, finance, and image recognition, as it supports automated decision-making. Achieving high accuracy in classification involves choosing the best algorithm for the specific dataset, because different algorithms perform differently depending on the data’s distribution and features. The present study examines and compares the effectiveness of three popular classification algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. Each algorithm employs a distinct classification strategy - KNN is based on distance-based learning, SVM focuses on constructing optimal decision boundaries, and Random Forest leverages multiple decision trees to enhance accuracy. To evaluate their performance thoroughly, we conduct experiments on two separate datasets, assessing their ability to accurately classify instances across varying conditions. Our assessment aims to determine the highest and lowest performing scenarios for each algorithm, considering factors like accuracy, computational efficiency, and resilience to data variations. By examining the results, we offer valuable insights into the strengths and weaknesses of these models, helping researchers and practitioners choose the most suitable classification algorithm for their specific needs. This study enhances the understanding of how various machine learning models perform across different datasets, ultimately supporting the creation of more dependable and efficient classification systems.