Document 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