Supervised Vs. Unsupervised Learning: A Comparative Study in Modern AI System

Abstract

The paper examines the differences between supervised and unsupervised learning in the modern AI context to evaluate the potential of the connecting approach for solving the research question of choosing the most suitable model for an application. In the following study, the quantitative data set obtained from finance, healthcare, and image recognition was used to analyze the model performance. The tasks were accomplished using Python along with TensorFlow and Scikit-learn environment where the algorithms used were decision tree, support vector machine, K-means clustering, and autoencoder. Thus, the accuracy of the supervised models is higher, about 92.4% of all datasets, whereas the unsupervised ones turned out to vary from 65.8% to 85.2%. On the other hand, unsupervised learning out competed itself in the processing of the unlabeled data especially in the areas of anomaly detection and pattern identification. The paper, therefore, notes that whereas high precision is promoted by supervised learning algorithm, approach is useful in exploratory ones especially where it is impossible to label the data. These findings help to advance the knowledge of practice regarding the effectiveness of using AI in real-life settings; more specifically, they underscore the relevance of using appropriate methods for learning depending on the nature of a task and the amount of data at one’s disposal.