A Comprehensive Review of the Literature on Supervised Learning

Abstract

Machine Learning (ML) is a fast developing area that opens up a wide range of creative methods for addressing practical issues. It is utilised in a number of applications, from fraud detection to recommendation systems and medical imaging, and it allows machines to learn from data without human interaction. The three primary subcategories of ML are supervised learning, unsupervised learning, and reinforcement learning. Pre-training the model on a labelled dataset is required for supervised learning, which combines classification and regression learning. In cases where the output is continuous, regression is applied. When the result is categorical, on the other hand, classification is utilised. Utilizing predictor characteristics, supervised learning tries to improve class label models.