Research

Choosing an evaluation strategy for machine learning research

Evaluation starts with the research question. Choose comparisons that test the claim you intend to make.

Match the question

Define the claim and the population it concerns. A convenient benchmark may not reflect the intended setting.

Separate development and evaluation

Plan splits before repeated experimentation. Consider leakage through related records, time, subjects or preprocessing.

Report uncertainty

Explain variation across runs and meaningful limitations. A single score rarely tells the whole story. Include appropriate baselines and error analysis.

Keep the record

Document decisions and exclusions. Clear reporting helps others understand what your evidence supports and what remains open.

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