lesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept driftlesson 2 of 6 · data drift and concept drift
Checkpoint
One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Build outcome monitoring with the label-delay tax made visible. From the eight predictions (half still awaiting labels), compute: coverage (fraction labeled), rolling accuracy over LABELED rows only, and print f"labels arrived: {k}/{n} ({cov:.0%})", f"rolling accuracy (labeled only): {acc:.0%}" — plus, when coverage is under 60%, the caution line "CAUTION: eval lags reality - most recent predictions unscored; watch leading indicators".