Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application

TL;DR

This paper explores innovative integration of machine learning algorithms with traditional statistical models, enhancing predictive accuracy, robustness, and interpretability in data analysis and decision-making.

Contribution

It introduces novel methods for combining ML algorithms with classical statistical models, demonstrating significant improvements in performance and flexibility.

Findings

Hybrid models improve predictive accuracy

ML integration enhances model robustness

New algorithms increase model interpretability

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