Machine Learning algorithms, perspectives, and real-world application: Empirical evidence from United States trade data

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. It is one of today’s most rapidly growing technical fields, lying at the crossroads of computer science and statistics, and at the core of artificial intelligence (AI) and data science. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in this area. Recent progress in ML has been driven both by the development of new learning algorithms theory, and by the ongoing explosion in the availability of vast amount of data (commonly known as “big-data”) and low-cost computation. The adoption of data-intensive ML-based methods can be found throughout science, technology, and commerce, leading to more evidence-based decision-making across many walks of life, including finance, manufacturing, international trade, economics, education, healthcare, marketing, policymaking, and data governance. The present paper provides a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and capabilities of an application. Moreover, the paper attempts to determine the accurate clusters of similar industries in United States that collectively account for more than 85 percent of economy’s aggregate export and import flows over the period 2002-2021 through clustering algorithm (unsupervised learning). Four clusters of mapping labels have been used, namely the low investment (LL), category 1 medium investment (HL), category 2 medium investment (LH) and high investment (HH). The empirical results indicate that machinery and electrical equipment is classified as a high investment sector due to its efficient production mechanism. The analysis further underlines the need for upstream value chain integration through skill-augmentation and innovation especially in low investment industries. Overall, this paper aims to explain the trends of ML approaches and their applicability in various real-world domains, as well as serve as a reference point for academia, industry professionals and policymakers particularly from a technical, ethical, and regulatory point of view.