How do you interpret an ROC curve, and what point matters?
mediumAnswer
- ROC plots True Positive Rate (sensitivity) vs False Positive Rate (1 - specificity) as the decision threshold sweeps from 1 to 0.
- The diagonal is random guessing; the top-left corner is perfect.
- AUC is the area under it — the probability that a random positive is scored higher than a random negative.
- The 'best' operating point depends on cost: often top-left-most, or the intersection with an iso-cost line matching your FP/FN penalties.
Check yourself — multiple choice
- ROC plots precision vs recall
- ROC plots TPR vs FPR; AUC = P(positive score > negative score); operating point depends on costs
- ROC only applies to regression
- ROC always requires balanced classes
ROC = TPR vs FPR. AUC = probability positive scored above negative. Operating point = cost-driven.
#metrics#classification
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