Supervised Machine Learning a Brief Survey of Approaches

Abstract

Machine learning has become popular across several disciplines right now. It enables machines to automatically learn from data and make predictions without the need for explicit programming or human intervention. Supervised machine learning is a popular approach to creating artificial intelligence. A computer algorithm is trained on input data that has been labeled for a certain output, making it one of two major areas of machine learning that has seen a lot of successful research. The model is trained until it can identify the underlying correlations and patterns between the input and output labels, enabling it to generate accurate labeling results when confronted with unexplored data. Supervised learning is good at solving classification and regression problems. The problem of regression occurs when the outputs are continuous, while the problem of classification occurs when the outputs are categorical. We will concentrate on the benefits and drawbacks of supervised learning algorithms in this review. Creating a precise model of the distribution of class labels in terms of predictor features is the aim of supervised learning. This work studied the most popular supervised machine learning methods, including Naive Bayes, Decision Trees, Support Vector Machines, Logistic Regression, K-Nearest Neighbors, and Deep Learning, which were discussed in this paper. We also emphasized the algorithms' advantages and disadvantages, and we ultimately talked about the difficulties in developing supervised machine learning algorithms.