Multi-Class Classification with the Gaussian Naive Bayes Algorithm

Abstract

Classification is a data mining technique involving supervised machine learning and is the process of predicting the class of data or dataset whose class is unknown using existing data with defined class. Supervised learning occurs during this classification process as a result of how this technique parses the data according to predetermined outputs. The Naive Bayes classifier is a type of machine learning algorithm and an approach that adopts Bayes’ theorem by combining theoretically obtained preliminary information with new information. The most obvious advantages of this classifier are its simple algorithm and high accuracy rate. The aim of this study is to create a classification model using the Gaussian Naive Bayes algorithm and to evaluate the obtained prediction results. For this purpose, the study first theoretically considers within its scope the Naive Bayes classifier and then carries out an application on a dataset using the Gaussian Naive Bayes algorithm as one of the types of this classifier in order to create a classification model, which is the subject of the study. Operations were carried out for the classification model using Python, an open-source programming language. The dataset used within the scope of the study was obtained from the University of California Irvine (UCI) Machine Learning Repository website. The purpose for creating the dataset is to determine the different types of Erythemato-squamous disease (ESD). In line with developing technologies, the number of studies demonstrating the ability to make fast and reliable disease prediction using machine learning techniques are increasing daily.

Keywords

• Gaussian Naive Bayes
• Supervised Machine Learning
• Classification
• Naive Bayes Classifier

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