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The Role of Feature Engineering in Machine Learning: Techniques, Challenges, and Automation with Data Engineering
Subject area: Science,Engineering and Technology · Area of research: Data engineering and machine learning
Abstract
Feature engineering is a crucial step in the machine learning (ML) pipeline, significantly impacting model performance by transforming raw data into meaningful features. This process involves selecting, creating, and transforming variables to enhance predictive accuracy and efficiency. Traditional feature engineering techniques include domain-specific feature selection, polynomial transformations, encoding categorical variables, and feature scaling. However, challenges such as high-dimensional data, data sparsity, and feature selection bias pose significant hurdles. With advancements in automation, feature engineering is increasingly integrated with data engineering workflows through tools like Feature Stores, AutoML, and deep learning-based feature extraction. Automated feature engineering streamlines the process, reducing manual effort and improving scalability, particularly in big data environments. This paper explores key techniques, challenges, and automation trends in feature engineering, highlighting its critical role in building robust machine learning models.
Keywords
Feature Engineering, Machine Learning, Data Engineering, Feature Selection, Automated Feature Engineering, Feature Stores, AutoML, High-Dimensional Data, Model Performance, Data Transformation
How to cite this paper
@article{1707510,
author = {Bhanu Prakash Reddy Rella},
title = {The Role of Feature Engineering in Machine Learning: Techniques, Challenges, and Automation with Data Engineering},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {805-823},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707510.pdf},
abstract = {Feature engineering is a crucial step in the machine learning (ML) pipeline, significantly impacting model performance by transforming raw data into meaningful features. This process involves selecting, creating, and transforming variables to enhance predictive accuracy and efficiency. Traditional feature engineering techniques include domain-specific feature selection, polynomial transformations, encoding categorical variables, and feature scaling. However, challenges such as high-dimensional data, data sparsity, and feature selection bias pose significant hurdles. With advancements in automation, feature engineering is increasingly integrated with data engineering workflows through tools like Feature Stores, AutoML, and deep learning-based feature extraction. Automated feature engineering streamlines the process, reducing manual effort and improving scalability, particularly in big data environments. This paper explores key techniques, challenges, and automation trends in feature engineering, highlighting its critical role in building robust machine learning models.},
keywords = {Feature Engineering, Machine Learning, Data Engineering, Feature Selection, Automated Feature Engineering, Feature Stores, AutoML, High-Dimensional Data, Model Performance, Data Transformation},
month = {April},
}