Module 4 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
Go deep on this one. It’s where most self-styled experts fall apart. Accuracy is a trap. On lopsided data, a model that predicts “nobody converts” can be right 95% of the time and be completely worthless.
What matters is precision and recall. Precision is, of the leads you flagged, how many were real. Recall is, of the real ones, how many you caught. F1 balances the two, and the confusion matrix shows you the trade you’re making. For scoring a list, the number is precision@k: of the top 50 leads you hand a rep, how many are worth their time. Then the move most people never make. Tie the model number to a money number, because a great score on paper can still lose money once it’s live.
Why it matters
This is your pitch with the proof attached. Anyone can say “every claim traces to a source” or “no hallucinations by design.” You can’t say it and then have no way to measure it. Being the person who can say “at precision@50, this list books X meetings” is the whole difference between an opinion and a claim.
Build this
The evaluation for the lead-scoring model from Module 2, written in plain business terms: at precision@50, this list books about X meetings. That one sentence is worth more than any certificate.
How to work through it
- StatQuest on precision and recall, ROC and AUC
- The classification units of Google’s crash course
- Compute precision@k on your lead-scoring output
- Turn the metric into a pipeline number
Resources
- StatQuest on precision and recall, ROC and AUC
- Google’s Machine Learning Crash Course, the classification units