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VACUUM

Training guidance principles From Wikipedia, the free encyclopedia

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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:

  • valid
  • accurate
  • consistent
  • uniform
  • unified
  • model
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