Integrating Neutrosophic Numbers in Regression Analysis for Enhancing Predictive Modelling through Uncertainty Representation

Abstract

This article explores the integration of neutrosophic statistics into regression analysis to bolster predictive modeling. Neutrosophic statistics, an extension of interval statistics, offer a robust framework for handling uncertainty and indeterminacy in datasets. By augmenting traditional interval predictions with measures of indeterminacy, neutrosophic numbers provide a nuanced representation of uncertainty, empowering decision-makers with a comprehensive view of potential outcomes. The methodology emphasizes the iterative refinement of predictive models to adapt to evolving data dynamics and escalating uncertainties. Future research directions include investigating the impact of different neutrosophic aggregation techniques on model performance, exploring synergies with other machine learning paradigms, and extending the applicability of neutrosophic statistics to diverse domains beyond regression analysis. By addressing these avenues, this study aims to advance the frontier of predictive modeling and facilitate more informed decision-making in complex and uncertain environments.