Data Science

FPR (False Positive Rate)

Arun Murali

Tags: data-science; evaluation; classification; fpr

🟪 1-Minute Summary

FPR (False Positive Rate) measures “of all actual negatives, how many did we incorrectly predict as positive?” Formula: FP / (FP + TN). Also called “fall-out”. Used in ROC curves (FPR on x-axis, TPR/Recall on y-axis). Lower FPR is better. Complement of specificity (Specificity = 1 - FPR = TN / (TN + FP)).

🟦 Core Notes (Must-Know)

Formula

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Interpretation

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FPR vs Specificity

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Role in ROC Curve

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🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

  1. “What does FPR measure?”

    • [Answer: Of actual negatives, how many did we incorrectly predict as positive]

  2. “What’s the relationship between FPR and specificity?”

    • [Answer: Specificity = 1 - FPR]

  3. “Why is FPR used in ROC curves?”

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🟥 Common Mistakes (Traps to Avoid)

Mistake 1: Confusing FPR with FNR

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🟩 Mini Example (Quick Application)

Scenario

[Calculate FPR from confusion matrix]

Solution

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🔗 Related Topics

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