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Detection and measurement
Deduction model
A scoring approach where each category starts at full marks and loses points only when a named check fires, rather than an opaque classifier returning a probability.
The verdict is exactly recomputable from the stored signals, so every deduction has a reason you can read on the dashboard. A classifier that returns a bare probability cannot tell an advertiser why a session scored low, which makes it hard to dispute or trust; the deduction model trades some modelling flexibility for an audit trail on every verdict.
Where ClickLens uses it: Why no black-box ML
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