Modern Methods for Machine Learning Algorithms: An Analysis of Supervised Learning Strategies for Data Categorization

Abstract

Artificial intelligence (AI) techniques such as machine learning (ML) enable a system to learn without explicit programming. The primary goal of the ML technique is to make computers capable of learning without human intervention. In terms of the supervised ML techniques, logistic regression is primarily employed to analyze information and prediction. Human input and output are essential for supervised algorithms, and they also need to provide feedback regarding the training process's prediction accuracy. In most ML decision-making tasks, we need to build a model that not only correctly interprets and collects the necessary input, but also, in the instance of controlled learning, generates reliable output predictions. In AI, the most widely used classification system is LR. The capacity to implement the method to fresh samples instead of describing patterns in the existing dataset sets it apart from independent learning. In contrast to supervised learning (SL) methods, which necessitate a training phase, unsupervised learning approaches do not. Nonetheless, SL techniques are less complicated than unsupervised techniques. The SL techniques that are frequently applied in the process of classifying data are reviewed in this study. The objective, approach, positive effects, along limitations of the methods are investigated. Finally, the target market is given an overview of supervised AI methods for data classification.