A Review of Feature Engineering Methods in Regression Problems

This paper comprehensively reviews the importance of feature engineering in regression problems and its existing methods. Different feature processing techniques, including data transformation, interactive feature generation, dimensionality reduction, and technical indicators, are analyzed, and some existing research results are combined to discuss how these techniques affect the predictive ability of the model. By systematically summarizing the advantages and disadvantages of various methods, this review provides researchers and practitioners with a comprehensive understanding of existing feature engineering techniques. In addition, this paper also explores the existing research gaps and possible future development directions, and proposes more application scenarios and research topics for related technologies.