Research Note V

Misuse and Overinterpretation of Evaluation Metrics

Evaluation metrics in machine learning research are often treated as definitive indicators of model quality, despite being context-dependent and limited in scope. Overreliance on single metrics or narrowly defined benchmarks can misrepresent real-world performance.

Effective research evaluation examines whether chosen metrics align with the research objective and whether alternative metrics might reveal important trade-offs or failure modes.

Reviewer note: Metrics should inform conclusions, not replace them.