Supervised Learning - A Systematic Literature Review

Machine Learning (ML) is a rapidly emerging field that enables a plethora of innovative approaches to solving real-worldproblems. It enables machines to learn without human intervention from data and is used in a variety of applications,from fraud detection to recommendation systems and medical imaging. Supervised learning, unsupervised learning, andreinforcement learning are the 3 main categories of ML. Supervised learning involves pre-training the model on a labeleddataset and entails two distinct types of learning: classification and regression. Regression is used when the output iscontinuous. By contrast, classification is used when the output is categorical.Supervised learning aims to optimize class label models using predictor features. Following that, a second classifieris used to assign class labels to the test data in cases where the values of the predictor characteristics are known butthe value of the class label is unknown. In classification, the label identifies the class to which the training set belongs.However, in regression, the label is a real-value response that corresponds to the example.