Bridging Scalability and Interpretability in AutoML Via Feature Engineering
TL;DR
Experimental evaluation on the Madelon dataset indicates that the automated and interpretable pipeline performs comparably to, and in some respects favourably against, baseline feature engineering approaches, demonstrating the practical effectiveness of combining scalable feature generation with interpretable AutoML.
Abstract
Feature engineering plays a key role in determining the performance of machine learning models, but manual feature design is time-consuming and requires strong domain knowledge. This work presents an automated feature engineering framework integrated with an interpretable AutoML pipeline, built around the BigFeat methodology. The system automatically generates new features from existing data using mathematical and logical operators and selects the most stable and relevant features for learning, while preserving interpretability by maintaining traceable mappings between original and engineered features. The framework is designed to handle large, high-dimensional datasets with manageable computational overhead. Automated model selection and hyperparameter tuning across Random Forest, Logistic Regression, and Decision Tree classifiers are incorporated to optimize predictive performance without manual intervention. The proposed system is intended to reduce human effort and development time in the feature engineering process while remaining scalable and adaptable to different datasets. Experimental evaluation on the Madelon dataset, a high-dimensional synthetic benchmark for feature selection, indicates that the automated and interpretable pipeline performs comparably to, and in some respects favourably against, baseline feature engineering approaches, demonstrating the practical effectiveness of combining scalable feature generation with interpretable AutoML.