A Paradigm for Potential Model Performance Improvement in Classification and Regression Problems. A Proof of Concept
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910318538522624 |
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| author | Lobo-Cabrera, Francisco Javier |
| author_facet | Lobo-Cabrera, Francisco Javier |
| contents | A methodology that seeks to enhance model prediction performance is presented. The method involves generating multiple auxiliary models that capture relationships between attributes as a function of each other. Such information serves to generate additional informative columns in the dataset that can potentially enhance target prediction. A proof of case and related code is provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Paradigm for Potential Model Performance Improvement in Classification and Regression Problems. A Proof of Concept Lobo-Cabrera, Francisco Javier Machine Learning A methodology that seeks to enhance model prediction performance is presented. The method involves generating multiple auxiliary models that capture relationships between attributes as a function of each other. Such information serves to generate additional informative columns in the dataset that can potentially enhance target prediction. A proof of case and related code is provided. |
| title | A Paradigm for Potential Model Performance Improvement in Classification and Regression Problems. A Proof of Concept |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.02354 |