Freeze the threshold and positive meaning
Owner role · Evaluation owner
Evidence: Positive means ready; equality at 0.5 counts as ready.
Forecast evaluation / Delivery leads, planning owners and acceptance reviewers
Acceptance rubricInspect false positives and false negatives at a decision threshold
Keep the consequences of a binary work-readiness classification visible beside accuracy.
This maps a fixed threshold to four case-level outcome cells, distinct from probabilistic scoring or majority-class accuracy.
Opens the current invite-request page. Access is subject to approval; this example is not imported automatically.
Original worked case · Manual planning resource
Inspect the decision, not just the summary.
Start with the invented evidence, follow the reasoning, and retain its limits when adapting the brief.
02 · Follow the reasoning
How the case leads to a decision
At threshold 0.5, A and B predict ready while C and D predict not ready. Only A and D match observations: 2/4=50% accuracy. False-ready B and missed-ready C have different planning consequences.
The bounded result
The rule has one true-ready, one false-ready, one missed-ready and one true-not-ready case. Keep the two error types separate rather than reporting only 50% accuracy.
01 · Inspect the inputs
Every record stays visible
| Case | Ready probability | Observed ready | Predicted ready | Category |
|---|---|---|---|---|
| A | 0.9 | 1 | 1 | True positive |
| B | 0.7 | 0 | 1 | False positive |
| C | 0.4 | 1 | 0 | False negative |
| D | 0.2 | 0 | 0 | True negative |
Use horizontal scrolling for wide tables. These records are invented, not customer data.
Definitions and method context
Records, decisions and policies are original synthetic examples. External references supply context; they do not validate these cases or TeamBoostAI capabilities.
Original worked-case definitions
Definitions, policy choices, records and calculations are authored for this worksheet. No external standard, statistical validation or live product measurement is claimed.
External references checked 6 October 2026. Demand for these topics has not been measured.
Review the acceptance boundary
Illustrative acceptance rubricSynthetic planning case: Four cases have supplied ready probabilities 0.9,0.7,0.4,0.2 and observed ready labels 1,0,1,0. The declared rule predicts ready when probability is at least 0.5.
| Criterion | Evidence | If missing | Your review |
|---|---|---|---|
| False-ready case B | 0.7 predicts ready; observed not ready | Inspect premature commitment consequence | |
| Missed-ready case C | 0.4 predicts not ready; observed ready | Inspect unnecessary wait consequence | |
| Correct cases A and D | Match under supplied rule | Do not erase the two different errors |
0 of 3 checks marked locally.
Marking a row records your review on this page; it does not verify evidence, save to an account or approve product work.
Decision to make: The rule has one true-ready, one false-ready, one missed-ready and one true-not-ready case. Keep the two error types separate rather than reporting only 50% accuracy.
Retain each case classification
Owner role · Reviewer
Evidence: A/B/C/D map to all four named cells under the declared rule.
Review error consequences separately
Owner role · Decision owner
Evidence: An early commitment and an unnecessary wait get distinct actions rather than an undifferentiated error count.
Filled manual planning note
Invented planning text. Adapt it to your evidence and confirmed owners.
The rule has one true-ready, one false-ready, one missed-ready and one true-not-ready case. Keep the two error types separate rather than reporting only 50% accuracy. At threshold 0.5, A and B predict ready while C and D predict not ready. Only A and D match observations: 2/4=50% accuracy. False-ready B and missed-ready C have different planning consequences. No real classifier, calibrated readiness forecast or automatic TeamBoostAI decision is asserted.
Put the outline to work
- Freeze the threshold and positive meaning. Check: Positive means ready; equality at 0.5 counts as ready.
- Retain each case classification. Check: A/B/C/D map to all four named cells under the declared rule.
- Review error consequences separately. Check: An early commitment and an unnecessary wait get distinct actions rather than an undifferentiated error count.
Questions about this workflow
Does this threshold have optimal performance?
No. It is a supplied rule; choosing a threshold requires an objective and appropriate evaluation.
Can these counts be treated as probabilities?
They describe four invented cases, not future error rates.
Does this create tasks in the product?
No. The brief is a manual planning resource. Use the product access link to check onboarding and the workflows available in your account.
From a useful outline to team work
Explore TeamBoostAI for your team
Use the owned checks and downloaded brief to discuss this planning decision alongside your TeamBoostAI tasks. Confirm available fields, roles and account features separately. The example is manual; it does not calculate live analytics, create work or run an experiment in the product. Confirm the workflows available in your account before adopting this outline.
Request TeamBoostAI access
Opens the current invite-request page. Access is subject to approval; this example is not imported automatically.