McNemar Test

McNemar’s test is especially useful when comparing two classification models on the same test set. The key question is not just whether their overall accuracy differs, but whether they make different errors on the same examples. In that sense, it goes beyond simple aggregate performance comparison and examines paired error behavior. This can provide a more sensitive and fair evaluation in model comparison. It is particularly useful in classification benchmarks and pre-deployment model selection workflows.