A detector caught 219 of 629 attacks across four held-out suites, a pooled rate of 35%. But the suite-level rates ranged from 1% to 100%. The aggregate describes the combined examples; it does not show how unevenly results varied between suites.
What the 35% pooled rate means
The DEV Community post reports 219 attacks caught out of 629 total across its four held-out suites. Dividing the combined catches by the combined attacks gives about 34.8%, rounded to 35%. This is a count-weighted aggregate: suites with more examples contribute more to the overall figure.
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That pooled rate answers, “What fraction of all reported attacks across these suites did the detector catch?” It does not answer whether the detector performed similarly in each suite.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteHow the four held-out suites differed
| Suite | Attacks caught | Reported rate |
|---|---|---|
| Workspace | 55 of 240 | 23% |
| Travel | 140 of 140 | 100% |
| Banking | 23 of 144 | 16% |
| Slack | 1 of 105 | 1% |
The displayed suite rates span 99 percentage points, from 1% to 100%. That spread is calculated from the reported endpoints; it is not a separate statistic reported by the post. Together, the table and the aggregate show why a single pooled percentage can conceal important differences.
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Why counts matter alongside percentages
A rate is easier to interpret when its numerator and denominator are visible. For example, the 1% Slack result represents 1 catch among 105 attacks, while the 100% travel result represents 140 catches among 140 attacks. The percentages describe the observed proportions; the counts show how many examples produced them.
The post’s excerpt does not establish how the suites were constructed or whether they represent likely deployment environments. These four results therefore should not be treated as a prediction for every domain, an independently replicated benchmark, or a general expected detector rate. The available figures also do not establish confidence intervals or statistical significance.
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Calibration budgets and held-out false alarms are different measures
The post distinguishes a threshold-selection budget enforced using benign scores on a calibration split from false alarms measured on held-out suites. The first constrains how a threshold is selected using calibration data. The second records observed false-alarm behavior on separate evaluation data. They answer different questions: meeting a calibration-time selection constraint does not guarantee the same false-alarm rate on held-out data.
When reporting a detector evaluation, state which data each metric uses. A calibration constraint and a held-out measurement should not be presented as though they were interchangeable evidence about performance.
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How to read or report results like these
- Show the pooled numerator and denominator, not just the rounded percentage.
- Report each evaluation suite’s rate and sample count beside the aggregate.
- Identify whether each metric comes from calibration data or held-out evaluation data.
- Describe the evaluated suites without implying they represent deployment domains unless that relationship is established.
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