Comparing Supervised and Unsupervised Learning Approaches for Specific Tasks

Abstract

Nearly all prior knowledge in machine learning can be categorised as either supervised learning (SL), which utilises labelled datasets for training, or unsupervised learning (UL), which infers latent structure in unlabelled data. Although both paradigms are well studied in isolation, they are rarely systematically compared across task domains. In this paper, we provide a differentiable comparison of SL and UL across six prototypical tasks—image classification, fraud detection, natural language sentiment analysis, customer segmentation, anomaly detection, and product recommendations—using twelve benchmark datasets. We consider accuracy, F1-score, silhouette coefficient, training time, memory usage, and label dependency. Our experiments show that SL outperforms UL on precision–critical tasks with sufficient labels (mean accuracy increase: +14.3%), while UL outperforms SL on exploratory and scalability–critical tasks by up to 38% according to cluster quality metrics. Additionally, we develop a decision framework to guide practitioners in choosing the right paradigm for given task constraints. All artefacts from the experiments—code, hyperparameter logs, and datasets—are publicly available.