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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14721 |
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| _version_ | 1866913563348566016 |
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| author | Mani, A |
| author_facet | Mani, A |
| contents | The concepts of precision, and accuracy are domain and problem dependent. The simplified numeric hard and soft measures used in the fields of statistical learning, many types of machine learning, and binary or multiclass classification problems are known to be of limited use for understanding the meaningfulness of models or their relevance. Arguably, they are neither of patterns nor proofs. Further, there are no good measures or representations for analogous concepts in the cognition domain. In this research, the key issues are reflected upon, and a compositional knowledge representation approach in a minimalist general rough framework is proposed for the problem contexts. The latter is general enough to cover most application contexts, and may be applicable in the light of improved computational tools available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Representation of Meaningful Precision, and Accuracy Mani, A Artificial Intelligence Machine Learning Logic in Computer Science 68T30, 68T37, 03G25 The concepts of precision, and accuracy are domain and problem dependent. The simplified numeric hard and soft measures used in the fields of statistical learning, many types of machine learning, and binary or multiclass classification problems are known to be of limited use for understanding the meaningfulness of models or their relevance. Arguably, they are neither of patterns nor proofs. Further, there are no good measures or representations for analogous concepts in the cognition domain. In this research, the key issues are reflected upon, and a compositional knowledge representation approach in a minimalist general rough framework is proposed for the problem contexts. The latter is general enough to cover most application contexts, and may be applicable in the light of improved computational tools available. |
| title | The Representation of Meaningful Precision, and Accuracy |
| topic | Artificial Intelligence Machine Learning Logic in Computer Science 68T30, 68T37, 03G25 |
| url | https://arxiv.org/abs/2410.14721 |