The Synergy between Machine Learning and Statistics: A Review

Abstract

Underscoring the interwoven methodologies and shared objectives of Machine Learning (ML) and Statistics, this paper aims to explore the synergy between the two disciplines with the proliferation of large datasets and advanced computational power. The ability of observing accurate insights for complex datasets and addressing real world applications with sophisticated, hybrid approaches can enhance with the convergence of the ML and Statistics. Although the concepts of both disciplines started with distinct origins, two disciplines increasingly intersect, fostering methodological cross fertilization. To improve the generalization and interpretability of ML concepts, statistical techniques such as model selection and regularization can be used while ensemble methods and neural networks exemplify predictive modeling’s statistical applications. By integrating ML to address the challenges in statistics such as fairness, interpretability, robustness, and scalability, statistician can enhance the key feature of statistics more effectively. Overall, combined concepts of ML and statistics not only address the diverse analytical task but it pave the path for Artificial Intelligence and data science by highlighting the main role of their synergy in modern data exploration.