Stability across conditions and the costs of errors: Which evidence should govern deployment?
Deployment requires robustness across conditions and analysis of the consequences of errors, not one accuracy score.
The question
An energy company’s maintenance model correctly identified 98% of failures in one retrospective sample, but its predictions were unstable across nearby time windows and sensor conditions. Maintenance capacity is limited, so false alarms also disrupt operations. Which evidence should govern deployment?
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- The 98% accuracy result alone.A single retrospective accuracy result does not establish dependable performance under the changing conditions already identified.
- A larger retrospective sample from the same conditions.More observations from unchanged conditions may reduce sampling uncertainty but do not resolve cross-condition instability or false-alarm consequences.
- A narrative explanation for each prediction, alongside the existing retrospective accuracy report.Explanations may support review, but they do not demonstrate stable performance or quantify the operational cost of errors.
- Stability across conditions and the costs of errors. ✓Deployment evidence must test consistency across relevant time and sensor conditions and quantify the operational consequences of false alarms and missed failures.
The trap
When performance varies by condition, seek robustness testing and error-consequence analysis rather than a larger headline score. How to remember it
Deployment requires robustness across conditions and analysis of the consequences of errors, not one accuracy score.
How many of these would you get right?
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