2. A method of determining performance of a machine learning apparatus of the type that generates a grouping from a plurality of documents, comprising: using at least one processor to perform each of the following steps: associating a precision probability distribution to said grouping; initially setting said precision probability distribution to a predetermined distribution; receiving review information about a first document associated with the grouping and assigning that first document a first document rating indicating whether the machine learning apparatus correctly or incorrectly assigned that first document to the proper grouping; applying Bayesian analysis using said first document rating to update said precision probability distribution; performing the following steps (a) and (b) until termination by a stopping rule that includes analyzing the relation of a grouping precision to at least one predetermined threshold: (a) successively processing subsequent documents associated with said grouping by assigning that subsequent document a subsequent document rating indicating whether the machine learning apparatus correctly or incorrectly assigned that subsequent document to the proper grouping; (b) applying Bayesian analysis using said subsequent document rating and the updated precision probability distribution to further update the precision probability distribution and to thereby generate said grouping precision; using the grouping precision as a metric to assess performance of said machine learning system.
10. The method of claim 2 wherein said stopping rule comprises a rejection rule where the grouping precision is below a predetermined threshold.