What is to be gained by ensemble models in analysis of spectroscopic data?
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910397105176576 |
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| author | Domijan, Katarina |
| author_facet | Domijan, Katarina |
| contents | An empirical study was carried out to compare different implementations of ensemble models aimed at improving prediction in spectroscopic data. A wide range of candidate models were fitted to benchmark datasets from regression and classification settings. A statistical analysis using linear mixed model was carried out on prediction performance criteria resulting from model fits over random splits of the data. The results showed that the ensemble classifiers were able to consistently outperform candidate models in our application |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02184 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What is to be gained by ensemble models in analysis of spectroscopic data? Domijan, Katarina Machine Learning Methodology An empirical study was carried out to compare different implementations of ensemble models aimed at improving prediction in spectroscopic data. A wide range of candidate models were fitted to benchmark datasets from regression and classification settings. A statistical analysis using linear mixed model was carried out on prediction performance criteria resulting from model fits over random splits of the data. The results showed that the ensemble classifiers were able to consistently outperform candidate models in our application |
| title | What is to be gained by ensemble models in analysis of spectroscopic data? |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2404.02184 |