A one class classification set-up for a set of rules acting as a model, where

  • each input is a whole dataset
  • model makes some decision within the dataset for each entry
  • output is decisions made for each entry for that particular input
  • reference standard judges whether the output is correct, calling it a "non-positive" if even one decision made for any entry is incorrect

Does this set-up make sense? Since I only have a say on positives, does using precision based on this reference standard(ground truth) make sense? I feel like there are gaps in this logic but can't pin-point where.



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