An Overview of the Supervised Machine Learning

Supervised Machine Learning (SML) is the branch of machine learning that explores the possibilities of making use of the learning ability of models based on soft computing logic, by providing solutions with the problems.The process of building intelligent systems that can identify the underlying relationships and patterns is at the core of this topic.Supervised machine learning involves training a model using labeled data.In this approach, the model learns from a given dataset that contains input variables and corresponding output labels.The goal is to enable the model to make accurate predictions or decisions when presented with new, unseen data.Supervised learning algorithms are widely used in various applications, such as image recognition, natural language processing, and fraud detection.By leveraging labeled data, supervised machine learning enables the development of predictive models that can generalize patterns and make informed decisions.This paper is an attempt to study different Supervised Machine Learning classification techniques, and comparison between said supervised learning algorithms as well as identifying the suitability of such algorithms to appropriate problems.Supervised Learning is broadly considered to be based on following different machine learning algorithms: Decision Table, Random Forest, Naive Bayes, Support Vector Machine, Neural Networks, Decision Tree.machine learning tool.Naive Bayes and Random Forest classification algorithms are used extensively due to the better accuracy provided.