A Supervised Machine Learning Algorithms: Applications, Challenges, and Recommendations

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Abstract

Machine Learning (ML) is an advanced technology that empowers systems to acquire knowledge autonomously, eliminating the need for explicit programming. The fundamental objective of the machine learning paradigm is to equip computers with the ability to learn independently without human intervention. In the literature, categorization in data mining has received a lot of traction, with applications ranging from health to astronomy and finance to textual classification. The three learning methodologies in machine learning are supervised, unsupervised, and semi-supervised. Humans must give the appropriate input and output and offer feedback on the prediction accuracy throughout the training phase for supervised algorithms. Unsupervised learning methods differ from supervised learning methods because they do not require any training. However, supervised learning methods are more accessible to implement than unsupervised learning methods. This study looks at supervised learning algorithms commonly employed in data classification. The strategies are evaluated based on their objective, methodology, benefits, and drawbacks. It is anticipated that readers will be able to understand the supervised machine learning techniques for data classification.

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