A Note on the Interpretability of Machine Learning Algorithms (notice n° 608941)
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|---|---|
| fixed length control field | 01266cam a2200157 4500500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250121161415.0 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | fre |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | dc |
| 100 10 - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Guégan, Dominique |
| Relator term | author |
| 245 00 - TITLE STATEMENT | |
| Title | A Note on the Interpretability of Machine Learning Algorithms |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2020.<br/> |
| 500 ## - GENERAL NOTE | |
| General note | 9 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | To analyze the concept of interpretability associated with an ML algorithm, a distinction is made between “how” (How does a black box or a very complex algorithm work?) and “why” (Why does an algorithm produce such-and-such a result?). These questions appeal to many actors: users, professionals, regulators, etc. Using a formal, standardized framework, existing solutions are indicated by specifying which elements in the supply chain are impacted when answers are provided to the previous questions. This presentation, by standardizing notations, allows for a comparison of different approaches so as to highlight the specificity of each (their objectives and processes). This study is not exhaustive — the subject is far from closed… |
| 786 0# - DATA SOURCE ENTRY | |
| Note | Annales des Mines - Enjeux numériques | 38 | 4 | 2020-12-24 | p. 31-43 | 2607-9984 |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://shs.cairn.info/revue-enjeux-numeriques-2020-4-page-31?lang=en&redirect-ssocas=7080">https://shs.cairn.info/revue-enjeux-numeriques-2020-4-page-31?lang=en&redirect-ssocas=7080</a> |
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