Proposes Nash averaging to improve evaluation in machine learning.
problem Overwhelming choices in evaluation suites and attacks have diluted the basic model.
method Detailed analysis of evaluation scenarios leads to Nash averaging, which adapts to data redundancies.
result Nash averaging encourages maximally inclusive evaluation, reducing bias.