International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Dynamic Feature Engineering for Transparent Machine Learning: a Framework for Interpretable Model Explanations

Author(s) Apurva Reddy Kistampally
Country United States
Abstract This article introduces a comprehensive framework for dynamic feature engineering that enhances the
transparency and interpretability of machine learning models across various industries. The framework
addresses the critical challenge of making complex ML models more comprehensible to stakeholders
while maintaining high-performance standards. Through a multi-layered architecture incorporating
feature transformation, mapping methodologies, and modular templates, the system clearly explains
model decisions to technical and non-technical users. The framework demonstrates significant
improvements in model interpretability, stakeholder understanding, and operational efficiency across
financial, healthcare, and customer engagement applications. Organizations can achieve enhanced model
transparency without sacrificing accuracy by implementing structured feature mapping and automated
optimization techniques. The article presents a detailed analysis of implementation strategies,
performance metrics, and integration protocols, providing practitioners with actionable insights for
deploying interpretable ML solutions. The article contributes to the growing field of explainable AI by
offering a scalable, enterprise-ready framework that bridges the gap between technical complexity and
business understanding.
Keywords Feature Engineering, Machine Learning Interpretability, Model Transparency, Explainable AI, Enterprise Framework
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-12
Cite This Dynamic Feature Engineering for Transparent Machine Learning: a Framework for Interpretable Model Explanations - Apurva Reddy Kistampally - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32651
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32651
Short DOI https://doi.org/g8vgh4
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