🟪 1-Minute Summary
ROC (Receiver Operating Characteristic) curve plots TPR (recall) vs FPR at all classification thresholds. AUC (Area Under Curve) summarizes ROC in one number (0 to 1). AUC = 1 is perfect, 0.5 is random guessing. Use to evaluate model’s ability to distinguish classes across all thresholds, independent of class distribution. Better than accuracy for imbalanced data.
🟦 Core Notes (Must-Know)
What is ROC Curve?
[Content to be filled in]
How to Read ROC Curve
[Content to be filled in]
What is AUC?
[Content to be filled in]
Interpreting AUC Values
[Content to be filled in]
- AUC = 1.0: Perfect classifier
- AUC = 0.9-1.0: Excellent
- AUC = 0.8-0.9: Good
- AUC = 0.7-0.8: Fair
- AUC = 0.5-0.7: Poor
- AUC = 0.5: Random guessing
When to Use ROC-AUC
[Content to be filled in]
🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
-
“Explain the ROC curve”
- [Answer: Plots TPR vs FPR at all thresholds]
-
“What does AUC = 0.85 mean?”
- [Answer: 85% chance model ranks random positive higher than random negative]
-
“When would you use ROC-AUC vs precision-recall curve?”
- [Answer: ROC-AUC for balanced data, PR curve for imbalanced]
-
“What does a diagonal ROC curve mean?”
- [Answer: Random classifier (AUC = 0.5)]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using ROC-AUC for highly imbalanced data
[Content to be filled in - use PR curve instead]
Mistake 2: Thinking AUC tells you the best threshold
[Content to be filled in]
🟩 Mini Example (Quick Application)
Scenario
[Compare classifiers using ROC-AUC]
Solution
from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt
# Example to be filled in
🔗 Related Topics
Navigation: