Classification Metrics: ROC-AUC vs PR-AUC & F1
Threshold-Free Discrimination, Precision-Recall Curves & The Fallacy of Accuracy
#Evaluation#Metrics#ROC-AUC#PR-AUC#Precision#Recall#F1-Score
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Claiming an ML model is "99.9% accurate" when classifying rare credit card fraud where 99.9% of transactions are legitimate.
Why It Fails in Production:
A trivial dummy model that predicts "NOT FRAUD" for every single transaction achieves 99.9% accuracy while catching 0% of fraud.
Targeted Algorithm (Classification Metrics: ROC-AUC vs PR-AUC & F1)
Latency:0.01ms
Cost / 1M Ops:$0.00
Determinism:Statistically Sound Metric
Generative LLM Alternative
Latency:N/A
Cost / 1M Ops:N/A
Determinism:Misleading Metric Claims