How to report evaluation honestly with baselines, held-out splits, precision, recall, NDCG, and operational measures.
Define the decision before the metric
A metric is useful only when it describes the decision the model is supposed to improve. Classification, ranking, retrieval, generation, and anomaly detection require different evidence.
Keep train and test honest
A clean held-out test split protects the credibility of an evaluation. Leakage, repeated tuning against the test set, or ambiguous preprocessing can produce a metric that looks impressive but does not generalize.
Use baselines
A model should be compared to a simple baseline that answers what improvement actually came from the added complexity. Without a baseline, a single score has little context.
Report failure cases
Strong ML evaluation includes examples of incorrect predictions, edge cases, class imbalance, uncertainty, and limitations. Failure analysis often produces more engineering insight than another decimal place.
Connect offline metrics to operations
Latency, memory usage, throughput, and reliability can matter as much as offline model quality. A model that wins on a benchmark but misses the system budget is not the right production choice.
- State the problem before the tools.
- Expose the system boundary.
- Use metrics with context and limitations.
- Document one meaningful trade-off.
- Link to adjacent project or topic pages.
What is machine learning model evaluation?
How to report evaluation honestly with baselines, held-out splits, precision, recall, NDCG, and operational measures.
Why does it matter?
A metric is useful only when it describes the decision the model is supposed to improve. Classification, ranking, retrieval, generation, and anomaly detection require different evidence.