Machine Learning Classification Techniques: A comparative Study

Abstract

Machine learning is the study of computer algorithms that improve automatically with experience. In other words it is the ability of the computer program to acquire or develop new knowledge or skills from examples for optimising the performance of a computer or a mobile device. In this paper we apply machine learning techniques Bayes network, Logistic Regression, Decision Stump, J48, Random Forest, Random Tree and REPtree to build classifier models and compare them. In this study machine learning techniques are applied to agriculture data and for each classifier model correctly classified instances, incorrectly classified instances, model build time and kappa statistics are computed. The reported test results describe the applicability and effectiveness of the classification approach.