Evaluations and datasets · Lesson 10 of 18 · 7 min

Review metric tradeoffs

Precision and recall answer different questions. More retrieved items can improve recall while introducing irrelevant results; select the tradeoff for the actual task.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-evals-datasets-010.json. Separate the recorded facts from your assumptions.
Hint & worked example
Precision and recall answer different questions. More retrieved items can improve recall while introducing irrelevant results; select the tradeoff for the actual task.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-evals-datasets-010.json
```
Common mistake to avoid: High recall means every returned item is useful.
2
Isolate the deciding signal
Select the precision field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Review precision because half the returned evidence is irrelevant to a short-list task.
A successful JSON query isolates precision.
```
jq .precision /opt/ai-lab/cases/aiops-evals-datasets-010.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: review-precision, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-evals-datasets-010-decision.txt.
Hint & worked example
Review precision because half the returned evidence is irrelevant to a short-list task.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'review-precision' > /home/learner/lab/aiops-evals-datasets-010-decision.txt
```
Common mistake to avoid: High recall means every returned item is useful.

Understand the pattern

Precision and recall answer different questions. More retrieved items can improve recall while introducing irrelevant results; select the tradeoff for the actual task.

Interpret the evidence

Review precision because half the returned evidence is irrelevant to a short-list task.

A tempting mistake

High recall means every returned item is useful.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-evals-datasets-010.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "precision": 0.5,
  "recall": 0.95,
  "task": "short evidence list"
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .precision /opt/ai-lab/cases/aiops-evals-datasets-010.json

0.5

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'review-precision' > /home/learner/lab/aiops-evals-datasets-010-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “review-precision” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Review metric tradeoffs” justify?Make the change now and gather the missing evidence afterward.Review precision because half the returned evidence is irrelevant to a short-list task.High recall means every returned item is useful.

Apply it elsewhere

A colleague concludes: “High recall means every returned item is useful.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Inspect a data slice Try an incident drill

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Case methodology: evidence, limits, decision