Effects of Attributes Size on the Performance of Machine Learning Algorithms

Abstract

Machine leaning is an emerging technology in research, it is extend as a great tool to explore and study of any area where data are collected in huge amount. This involves analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision-making without the need for direct human interaction. It use in many areas such as health care, finance, marketing etc. as a tool of research and development. Machine learning tools will enable you to play with the data, train your models, discover new methods, and create algorithms. This paper presents the study of some well known Machine learning algorithms and the effect of attribute size on their performance in the term of accuracy. Experimental result shows that performance changes for some algorithms. Accuracy of Naïve bayes, Logistic regression, SMO are decreased as the number of attributes increased, while Random forest and J48 performance are same in both the cases.