Feature Engineering Optimization Methods for Multi-Domain Predictive Analytics: A Comprehensive Evaluation Study

Abstract

Feature engineering remains a critical component in predictive analytics across diverse application domains. This study presents a comprehensive evaluation of feature engineering optimization methods applied to multi-domain predictive tasks including financial risk assessment, healthcare analytics, and digital advertising. The research examines various feature selection techniques, transformation approaches, and their impact on model performance across different data characteristics. Through systematic experimentation on six real-world datasets, we analyze the effectiveness of filter-based, wrapper-based, and embedded feature selection methods. Our experimental results reveal domain-specific patterns in feature engineering effectiveness, with wrapper methods achieving 8.3% improvement in financial datasets while filter approaches demonstrate superior computational efficiency with only 2.1% performance trade-off. The study provides empirical evidence for selecting appropriate feature engineering strategies based on dataset properties and application requirements. Our findings contribute to establishing practical guidelines for practitioners implementing predictive analytics solutions across varied domains.