A Review of Statistical Modelling and Machine Learning in Analytical Problems

Abstract

Data scientists and statisticians often conflict when deciding on the best approach to solve analytical challenges through machine learning and statistical modeling. However, machine learning and statistical modeling complement each other. Machine learning and statistical modeling are essentially based on similar mathematical principles but use different tools to construct the overall analytical knowledge base. Determining the predominant approach to be employed should be based on the problem to be solved, as well as empirical evidence, such as the size and completeness of the data, number of variables, assumptions or lack thereof, and expected outcomes such as predictions or causality. Good analysts and data scientists thus should be aware of the inherent difference between the two methods based on their proper applications and tools to achieve the desired results.