Optimizing Feature Selection: A Comparative Analysis of AI Techniques in Automated Data Transformation

Abstract

Feature selection is a critical step in the data transformation process that significantly impacts the performance of machine learning models. This paper explores various AI techniques for optimizing feature selection, comparing their effectiveness in automated data transformation scenarios. By analyzing the strengths and weaknesses of different methods, this study aims to provide insights into best practices for enhancing model performance and interpretability.