Accuracy alone doesn't tell the whole story. Explore how the confusion matrix reveals what kinds of mistakes your classifier makes, and why that matters differently in each domain.
False negatives (missed cancer) are more dangerous than false positives (unnecessary tests).
Samples with score ≥ threshold are predicted positive. Lower threshold → more positives predicted.
In medical screening, a missed cancer (FN) can be fatal, so you lower the threshold to maximize recall, accepting more false alarms. In spam filtering, you'd rather let spam through than block a job offer.