Comparative Analysis of Machine Learning Algorithms : Random Forest algorithm, Naive Bayes Classifier and KNN - A survey

Abstract

Machine learning is a branch of computer science in which a computer predicts the next task to be performed by analysing the data that is provided to it. The computer can access data in the form of digitised training sets or through interaction with the environment. The primary goal of this paper is to provide a general comparison of the Random Forest algorithm, the Naive Bayes Classifier, and the KNN algorithm all aspects. "Random Forest Classifier" is made up of many decision trees. To promote uncorrelated forests, the algorithm leverages randomization to form each individual tree, which then uses the forest's predictive powers to make accurate decisions. The Naive Bayes Classifier is a simple and effective classification method that aids in the development of fast machine learning models capable of making quick predictions. “K-Nearest Neighbour”. The algorithm can be used to handle problems involving classification and regression. These algorithms are surveyed on the basis of aim, methodology, advantages and disadvantages.