A HANDS-ON EXPLORATION OF CORE SUPERVISED LEARNING TECHNIQUES

Abstract

ABSTRACT
Supervised Machine Learning (SML) is a key domain within machine learning that focuses on leveraging the learning capabilities of models grounded in soft computing principles to solve real-world problems. At its core, SML involves the development of intelligent systems capable of identifying patterns and relationships within data. This is achieved by training models on labeled datasets, where each input is associated with a corresponding output label. Through this process, the model learns to make accurate predictions or classifications when presented with new, unseen data.
Supervised learning techniques are extensively employed in diverse applications, including image recognition, natural language processing, and fraud detection. By utilizing labeled data, SML facilitates the creation of predictive models that can generalize learned patterns to make informed decisions.
This paper aims to examine various supervised machine learning classification methods, compare their performance, and assess their suitability for specific types of problems. Commonly used supervised learning algorithms include Decision Table, Random Forest, Naive Bayes, Support Vector Machine (SVM), Neural Networks, and Decision Tree. Among these, Naive Bayes and Random Forest are frequently applied due to their high accuracy and robustness across multiple domains.