DEVELOPING EXPLAINABLE STATISTICAL MODELS FOR ENHANCING INTERPRETABILITY AND TRUST IN MACHINE LEARNING APPLICATIONS
Keywords:
Explainable Machine Learning, Interpretability, Statistical Models, Trust in AI, Predictive ModelingAbstract
The demand for explainable machine learning (ML) models is growing, driven by applications where interpretability and trust are critical. This paper explores the development of explainable statistical models to enhance interpretability without compromising accuracy. By integrating statistical methods and modern ML techniques, the study provides insights into improving transparency in predictive modeling. The paper includes a detailed review of relevant literature, proposes methodologies for model development, and evaluates their applicability across different domains. Graphs and tables illustrate the findings to ensure clarity and accessibility.
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