Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917067354013696 |
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| author | Rao, A. Ganapathi Anumula, Sathish Krishna Singh, Aditya Kumar M, Renukhadevi Kumar, Y. Jeevan Nagendra Tulasi, Tammineni Rama |
| author_facet | Rao, A. Ganapathi Anumula, Sathish Krishna Singh, Aditya Kumar M, Renukhadevi Kumar, Y. Jeevan Nagendra Tulasi, Tammineni Rama |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04918 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application Rao, A. Ganapathi Anumula, Sathish Krishna Singh, Aditya Kumar M, Renukhadevi Kumar, Y. Jeevan Nagendra Tulasi, Tammineni Rama Machine Learning 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 |
| title | Machine Learning Algorithms in Statistical Modelling Bridging Theory and Application |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.04918 |