VACUUM

Last updated

VACUUM [1] [2] [3] [4] is a set of normative guidance principles for achieving training and test dataset quality for structured datasets in data science and machine learning. The garbage-in, garbage out principle motivates a solution to the problem of data quality but does not offer a specific solution. Unlike the majority of the ad-hoc data quality assessment metrics often used by practitioners [5] VACUUM specifies qualitative principles for data quality management and serves as a basis for defining more detailed quantitative metrics of data quality. [6]

VACUUM is an acronym that stands for:

References

  1. ↑ https://books.google.com/books?id=XPBbEAAAQBAJ&q=VACUUM
  2. ↑ "The VACUUM Model: Valid, Accurate, Consistent, Uniform, and Unified". archive.is. Archived from the original on 2024-10-21. Retrieved 2024-11-01.{{cite web}}: CS1 maint: bot: original URL status unknown (link)
  3. ↑ Jim Nasby (2015), All the Dirt on VACUUM, PGCon - PostgreSQL Conference for Users and Developers, Andrea Ross, retrieved 2021-04-27
  4. ↑ "An Overview of VACUUM Processing in PostgreSQL". Severalnines. 2019-11-22. Retrieved 2021-04-27.
  5. ↑ Pipino, Leo L.; Lee, Yang W.; Wang, Richard Y. (2002-04-01). "Data quality assessment" . Communications of the ACM. 45 (4): 211–218. doi:10.1145/505248.506010. ISSN   0001-0782. S2CID   426050.
  6. ↑ Wang, R.Y.; Storey, V.C.; Firth, C.P. (August 1995). "A framework for analysis of data quality research". IEEE Transactions on Knowledge and Data Engineering. 7 (4): 623–640. doi:10.1109/69.404034.


Hey Machine Learning Logo.png

This machine learning-related article is a stub. You can help Wikipedia by expanding it.

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.