Inspect the scores.
Scores are supplied, not calibrated probabilities.
A boundary turns a score into a decision.
INSTRUMENTS / MEASUREMENT & FRAMING
Constructed exampleMove the cutoff. Inspect the errors.
Change an input. Inspect every step. Keep a fixed record.
01 / THE QUESTION
What would you rather get wrong?
Twenty fictional messages have supplied labels and scores. A cutoff turns each score into a decision: flag it as spam, or let it through.
Keep a first thought
Optional. Included only when you explicitly save; excluded from shared links.
Constructed data · every relationship and outcome is supplied.
02 / OPEN THE MODEL
True positive7Spam flagged
False positive2Legitimate flagged
False negative1Spam passed
True negative10Legitimate passed
FOLLOW ONE MESSAGE
S07 · score 60 → flagged → true positive
Supplied label: spam. Equality is on the flagged side.
Assign costs to the mistakes
Whole fictional points 0–100. Changing a cost does not change any classification or move the cutoff.
2False positivesmessages
1False negativesmessages
9Assigned error costfictional points
Follow the idea.
A concept, in three framesMove the cutoff.
The decision changes at an explicit boundary.
Count both errors.
False positives and false negatives differ.
HOLD ONE VERSION STILL
What changes when you change the model?
Keep the current inputs beside your next adjustment.
A cutoff is a choice with consequences.
A false positive flags a legitimate message. A false negative lets spam through. Moving the cutoff changes which mistakes this fixed set contains.
Save this experiment
MODEL & LIMITS
What this model leaves open
These scores are not calibrated probabilities, and twenty supplied messages do not estimate real-world performance. Assigned costs are fictional points, not objective harm.
- InputsWhat is supplied?
- DefinitionWhat is counted?
- InferenceWhat follows?
Inspect the exact workings
| ID | Score | Supplied label | Decision | Outcome |
|---|---|---|---|---|
| S01 | 90 | Spam | Flag | True positive |
| S02 | 85 | Spam | Flag | True positive |
| S03 | 80 | Spam | Flag | True positive |
| S04 | 75 | Spam | Flag | True positive |
| S05 | 70 | Spam | Flag | True positive |
| S06 | 65 | Spam | Flag | True positive |
| S07 | 60 | Spam | Flag | True positive |
| S08 | 55 | Spam | Pass | False negative |
| L01 | 95 | Legitimate | Flag | False positive |
| L02 | 68 | Legitimate | Flag | False positive |
| L03 | 58 | Legitimate | Pass | True negative |
| L04 | 48 | Legitimate | Pass | True negative |
| L05 | 45 | Legitimate | Pass | True negative |
| L06 | 40 | Legitimate | Pass | True negative |
| L07 | 35 | Legitimate | Pass | True negative |
| L08 | 30 | Legitimate | Pass | True negative |
| L09 | 25 | Legitimate | Pass | True negative |
| L10 | 20 | Legitimate | Pass | True negative |
| L11 | 15 | Legitimate | Pass | True negative |
| L12 | 10 | Legitimate | Pass | True negative |
Precision: 7/9 = 77.78%. Recall: 7/8 = 87.50%. Assigned cost: 2 × 4 + 1 × 1 = 9 fictional points.
Assumptions, sources and evidence status
Original constructed fixture: constructed-messages-v1. thresholds-v1. No empirical observations are represented by these example counts.
- All labels and scores are supplied; these are not empirical classifier performance estimates.
- Scores are not calibrated probabilities. Error costs are fictional points chosen by the reader.
- Changing costs does not move the threshold or retrain a model. Precision is undefined if no messages are flagged.