Blog · AI in Practice
Seasonal Effects on Model Evaluation
A model evaluated in one season has been evaluated in one season. Reporting a single number from a single campaign is the most common evaluation error.
Overview
- Report results per season, with the sample count for each, or state clearly that the evaluation is single-season.
- Appearance changes with lighting, foliage, snow and moisture — the same asset can look like two different things.
- Seasonal change is not model error, but it produces the same operational effect; evaluate it as a condition, not as noise.
- A rolling evaluation set that grows with the seasons is more honest than a fixed one collected in a week.
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