Quantile Regression in Machine Learning: A Survey

The mean regression methodology may not be successful in developing precise mathematical models when working with data that has a significant number of outliers. The reason for this is that the outliers have a considerable impact on the mean value, making it untrustworthy for the intended usage. A different model termed Quantile Regression (QR) has become more popular as a solution to this problem. Quantile regression is more resistant to outliers and can handle data with a wide range of distributions. It has emerged as a promising approach for practical applications, offering a more comprehensive view than mean regression. In this paper, we delve deeper into this strategy, with a particular focus on its application in various fields, including the prediction of wind power as well as its usage in finance and economics research. Rather than just providing a technical explanation of the theory or a comprehensive analysis of recent developments, we begin by examining important applications to illustrate the usefulness of the approach. We then discuss these topics in further detail with a brief introduction to their mathematics. Finally, we provide an overview of current research topics that make substantial use of this method, later concluding why we need it.