A Framework for Integrating Domain Knowledge into Machine Learning Models for Improved Interpretability
Keywords:
interpretability, domain knowledge, machine learning, hybrid modeling, explainable AISynopsis
Modern machine learning (ML) models achieve impressive predictive performance but often lack interpretability, limiting trust and adoption in high-stakes domains such as healthcare, finance, and engineering. This paper proposes a structured framework for integrating domain knowledge directly into machine learning models to improve interpretability without sacrificing performance. We present systematic techniques combining expert rules, constraint-based learning, and feature engineering, and evaluate their impact across synthetic and real-world datasets. Results indicate that domain knowledge integration enhances model transparency, reduces uncertainty, and fosters actionable insights.
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