Calculator Inputs
Enter current and proposed model assumptions. The tool compares quality, workload, and monthly business value side by side.
Example Data Table
| Scenario | Monthly Volume | Positive Rate | Precision | Recall | Automation | Model Cost/Case | Fixed Monthly Cost |
|---|---|---|---|---|---|---|---|
| Baseline | 50,000 | 8% | 72% | 64% | 45% | $0.03 | $300 |
| Scenario | 50,000 | 8% | 81% | 78% | 68% | $0.05 | $750 |
Formula Used
Actual Positives = Monthly Volume × Positive Rate
Actual Negatives = Monthly Volume − Actual Positives
True Positives = Recall × Actual Positives
False Negatives = Actual Positives − True Positives
False Positives = True Positives × (1 − Precision) ÷ Precision
True Negatives = Actual Negatives − False Positives
F1 Score = 2 × Precision × Recall ÷ (Precision + Recall)
Accuracy = (True Positives + True Negatives) ÷ Monthly Volume
Manual Cases = Monthly Volume × (1 − Automation Rate)
Manual Review Cost = Manual Cases × Review Cost per Case
Net Monthly Value = TP Value − FP Cost − FN Cost − Review Cost − Model Cost − Fixed Monthly Cost
Payback Months = Scenario Setup Cost ÷ Monthly Improvement
This approach is useful for AI triage, lead scoring, fraud review, quality monitoring, content moderation, and decision-support workflows where both model quality and operating cost matter.
How to Use This Calculator
- Enter the monthly case volume and the actual positive rate for your task.
- Define business value for true positives and penalties for false positives and false negatives.
- Add manual review cost per case to reflect human oversight expense.
- Enter baseline precision, recall, automation rate, model cost, and fixed monthly cost.
- Enter the proposed scenario assumptions for the improved model or workflow.
- Click Run What-If Analysis to compare metrics, workloads, and monthly value.
- Review the recommendation, charts, and delta table for operational tradeoffs.
- Export the result set as CSV or PDF for stakeholder reviews or planning documents.
FAQs
1. What does this tool compare?
It compares a baseline AI workflow with a proposed scenario. You can estimate quality changes, false alerts, missed positives, review workload, monthly value, and payback speed in one place.
2. Why are precision and recall both needed?
Precision reflects alert quality, while recall reflects how many true positives are captured. Strong what-if analysis needs both because one metric alone can hide costly tradeoffs.
3. What is the actual positive rate?
It is the share of cases that truly belong to the positive class, such as fraud cases, qualified leads, or safety issues. This rate shapes the confusion matrix and business value.
4. Can I use this for classification projects only?
It is best for classification or alerting systems, but the framework can also guide ranking, triage, moderation, and review-heavy workflows where decisions have measurable rewards and penalties.
5. Why does automation rate matter here?
Automation rate affects how many cases still require manual review. Even a better model can underperform financially if review volume remains high or human handling cost is expensive.
6. What if my inputs are unrealistic?
The calculator checks for impossible combinations, such as more false positives than available negatives. When needed, it caps values and shows a warning so the comparison stays feasible.
7. How should I estimate value and cost inputs?
Use historical outcomes, analyst time, customer impact, revenue uplift, or loss prevention numbers. The tool becomes more useful when those inputs reflect real operational evidence.
8. What makes a scenario worth adopting?
A strong scenario usually improves net monthly value, lifts F1 or recall meaningfully, keeps false positives manageable, and recovers setup cost within an acceptable payback window.