Tag: MachineLearning
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When the Ruler Moves With the System
A changing score does not necessarily mean the system changed. The evaluator may have changed too. Datasets evolve, parsers are corrected, thresholds move, admissibility rules shift, and reporting conventions are revised. Once both the artifact and the evaluator are moving, a numerical difference can be completely real and still fail to tell us what actually…
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Your A/B Test Should Be Testing Itself
A/B testing usually asks which variant should change. Far less often do we ask whether the experiment itself should change: the number of arms, allocation policy, stopping rule, promotion threshold, replay strategy, or quality controls that determine what the organization is able to learn. That becomes especially important in adaptive and AI-driven systems, where the…