Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application.

Authors

  • A. Ganapathi Rao
  • Sathish Krishna Anumula
  • Aditya Kumar Singh
  • Renukhadevi M
  • Y. Jeevan Nagendra Kumar
  • Tammineni Rama Tulasi

Keywords:

Cross-Disciplinary Applications, Interpretability, Algorithmic Bias, Dimensionality Reduction, Hybrid Models, Predictive Analytics, Classification, Regression, Statistical Modelling, Machine Learning

Abstract

It involves the completely novel ways of integrating ML algorithms with traditional statistical modelling that has changed the way we analyze data, do predictive analytics or make decisions in the fields of the data. In this paper, we study some ML and statistical model connections to understand ways in which some modern ML algorithms help 'enrich' conventional models; we demonstrate how new algorithms improve performance, scale, flexibility and robustness of the traditional models. It shows that the hybrid models are of great improvement in predictive accuracy, robustness, and interpretability

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Published

2025-05-08

How to Cite

1.
Ganapathi Rao A, Krishna Anumula S, Kumar Singh A, M R, Jeevan Nagendra Kumar Y, Rama Tulasi T. Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application. J Neonatal Surg [Internet]. 2025May8 [cited 2025Sep.21];14(21S):672-80. Available from: https://jneonatalsurg.com/index.php/jns/article/view/5350

Issue

Vol. 14 No. 21S (2025): Journal of Neonatal Surgery

Section

Original Article

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