Compare training distributions with deployment conditions: Which evidence most directly resolves that
Compare training-record distributions with the conditions expected in deployment.
The question
An energy company plans predictive maintenance across five regions. Approval depends specifically on whether the training records represent the equipment types, climates, and failure patterns expected in deployment. Which evidence most directly resolves that uncertainty?
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- Report aggregate accuracy onlyAggregate accuracy can hide missing equipment types, climates, or failure patterns and does not establish training-data representativeness.
- Seek engineer approval after reviewing historical failure examplesEngineer review can add contextual judgment, but it does not systematically demonstrate that training records represent the deployment population.
- Compare training and test counts by regionCounts by region show quantity and partitioning, but not whether the training records reflect the relevant equipment, climates, and failures.
- Compare training distributions with deployment conditions ✓Comparing training-record distributions with actual deployment conditions directly identifies representation gaps in equipment, climates, and failure patterns.
The trap
Match the evidence to the uncertainty: population representativeness requires comparing data distributions with deployment conditions. How to remember it
Compare training-record distributions with the conditions expected in deployment.
How many of these would you get right?
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