Challenges in Supervised and Unsupervised Learning: A Comprehensive Overview

Abstract

Data science and machine learning are at the forefront of modern technological advancements, promising automated insights, predictions, and decision-making. Supervised and unsupervised learning are pivotal paradigms within this dynamic landscape, each presenting its unique challenges. This article provides a comprehensive overview of the multifaceted challenges inherent to both supervised and unsupervised learning. This article reviews research studies published between 2019 and 2023. This article discusses the challenges of supervised and unsupervised learning. In supervised learning, challenges include data labeling, overfitting, limited generalization, and balancing mistake equivalence and decision-making goals. In unsupervised learning, difficulties encompass issues like overfitting, choosing the appropriate algorithm, and interpreting results. This includes evaluating the quality of clustering, deciding the optimal number of clusters, and managing noise and outliers. The article aims to provide insights into these challenges, enhancing the understanding of machine learning for both novices and experts. Researchers and practitioners constantly evolve their methods and tools to overcome these complexities. This article is a valuable reference for researchers and experts in the field, empowering them to navigate these challenges confidently. As technology advances, a thorough understanding of these challenges is essential for unlocking the full potential of these powerful tools. Finally, several recommendations were given to guide future researchers in applying machine learning in the journey of data-driven discovery and automation, offering challenges and opportunities for those who embark on it.