Overview and advantages of Machine Learning (ML) in Statistics

Abstract

Object: The main purpose of this study is to provide insight into why machine learning is the future of statistics. The virtual world generated a vast amount of data bringing together intelligent machines and networked processes. Machine learning as the emerging field of data science leads to new implications for statistics in terms of the big data era. Nowadays Machine Learning (ML) application is becoming broader including psychology, artificial intelligence, control theory, information theory, neuroscience, philosophy, Bayesian method, computational complexity theory etc. The recent use of ML in medicine, agriculture or trading is evidence of its future development in the coming years. Methods: This study is based on the literature review of Machine learning (ML) models, paradigms, algorithms, and their advantages versa classical statistics. As obvious of ML application, the number of articles on Machine Learning and Data Science vs Classical Statistics in Wikipedia reflected in Python. Findings: The main results of this study are listing the main Machine Learning Algorithms and applications. In addition, this paper identifies the main advantages and disadvantages of Machine Learning versa classical statistics. Conclusions: There are many advantages of Machine Learning (ML), which highlight the future of Machine learning methods in statistics. The increase in data and innovations make a long and broad way of Machine Learning (ML) development.