Automated Feature Engineering in Machine Learning: Enhancing Model Performance with Minimal Human Intervention

Abstract

To extract useful features from raw data, feature engineering—a vital stage in the machine learning pipeline—often necessitates domain knowledge and substantial human labor. But now there's an automated feature engineering tool that can do all that and more, improving model performance with less human input. several automated feature engineering frameworks and methods, such as genetic algorithms, deep feature synthesis, and systems based on reinforcement learning. These strategies seek to enhance the learning process and the predictive power of models across varied datasets by automating feature generation, selection, and transformation. We show that automated feature engineering approaches significantly increase model accuracy, interpretability, and efficiency across a variety of applications, including classification, regression, and time-series analysis. Based on our findings, automated feature engineering can reduce the need for human feature engineers, speed up model building, and improve generalizability to other problem domains.