IMPROVING THE PERFORMANCE OF THE PREDICTION BY MACHINE LEARNING ALGORITHMS

Abstract

Over the past few decades, Machine Learning
(ML) has evolved from the endeavor of few computer enthusiasts
exploiting the possibility of computers learning to play games,
and a part of Mathematics (Statistics) that seldom considered
computational approaches, to an independent research
discipline that has not only provided the necessary base for
statistical-computational principles of learning procedures, but
also has developed various algorithms that are regularly used for
text interpretation, pattern recognition, and a many other
commercial purposes and has led to a separate research interest
in data mining to identify hidden regularities or irregularities in
social data that growing by second. This paper focuses on
explaining the concept and evolution of Machine Learning,
some of the popular Machine Learning algorithms and try to
compare three most popular algorithms based on some basic
notions. Sentiment140 dataset was used and performance of
each algorithm in terms of training time, prediction time and
accuracy of prediction have been documented and compared.