Comparison of Decision Tree Classifier and Bayes Classifier using WEKA

Abstract

Data Mining is the process of locating potentially practical, interesting and previously unknown patterns from a big volume of data. It plays an important role in result orientation. Data mining can be used in each and every aspect of life. The same is similarly significant in other areas including sales/ marketing, revenue services, sports, health care and insurance etc. Classification is used to builds models from data with predefined classes as the model is used to classify new instance whose classification is not known. This paper compares the two famous algorithms called Bayesian and Decision tree algorithm and how it works on nominal and numerical data sets and demonstrates its results. The accuracy, precision, and classification errors are also measured to compare algorithm. WEKA tool has been used to perform the experiment.