Precision, Recall & F1
Precision and recall are the two error rates that survive imbalance: precision asks "can I trust an alarm?", recall asks "did I catch them all?". F1 is their honest harmonic compromise, and the precision-recall curve is how you pick the operating point the business actually wants.
01.The Problem: Two Ways to Fake a Perfect Model
You built a fraud detector.
Management asks one innocent question:
"How good is it?"
And here is the trap — two useless models can both answer "perfect".
- Model A flags every single transaction. It catches every fraudster, guaranteed. Recall = 1.0! It also blocks every honest customer. Useless.
- Model B flags one transaction a month — the one it is totally sure about. When it speaks, it is right 100% of the time. Precision = 1.0! It misses 99.9% of fraud. Also useless.
So what do you actually report?
You report two numbers instead of one:
- Precision — when the model raises an alarm, can you trust it?
- Recall — of all the real cases, how many did you catch?
These are the two error rates that survive class imbalance (when one class is rare — see Topic 70's spam example, where accuracy said "98%" about a filter that caught nothing).
The Precision-Recall Seesaw ⚖️
The Precision-Recall Seesaw ⚖️
One score distribution, many thresholds: tightening precision always trades away recall. The F1 picks the compromise; the PR curve lets a stakeholder pick it instead.
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