Comparing the Performance of Different Supervised Learning Algorithms

Abstract

In recent years, machine learning has grown in popularity, with applications in a wide range of industries. This study discusses the fundamentals of machine learning and its various approaches, such as supervised classifier, unsupervised classifier and reinforcement learning. Moreover, the drawbacks of machine learning, such as the requirement for a lot of labelled data, the possibility of bias in the training process etc. are studied. Finally, some of the field's potential future developments, such as the use of machine learning in areas like healthcare and finance, have been elaborated. Furthermore, two algorithms of machine learning such as Decision Tree and Naive Bayes algorithms are compared. Overall, this work provides a thorough overview of machine learning's current state and potential future impact.