Explainable Machine Learning Frameworks for Intelligent Decision-Making in Enterprise Applications
Abstract
Explainable Machine Learning (XML) frameworks are increasingly important for enabling transparent, trustworthy, and intelligent decision-making in complex enterprise applications. This study proposes an explainable machine learning framework that integrates predictive analytics, model interpretability, enterprise data processing, and decision intelligence to support reliable business operations. The framework combines machine learning algorithms with explainability techniques, including feature importance analysis, SHAP-based interpretation, and local and global explanation methods, to identify the factors influencing predictive outcomes. Enterprise data from operational, financial, customer, and security-related sources can be processed to generate actionable insights while maintaining transparency throughout the decision-making process.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 5 No. 1 (2020): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
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Published |
January 7, 2020 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sander Hoogendoorn (%2020). Explainable Machine Learning Frameworks for Intelligent Decision-Making in Enterprise Applications. International Journal of Emerging Trends in Engineering and Management Research , Vol. 5 No. 1 (2020): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) . https://doi.org/10.15662/x28mwc39 |
References
No references available for this article