Use case 01
The default probability threshold is unlikely to be optimal.
Data ScientistModel EvaluationIntermediateSingle prompt
Threshold Optimization AI Prompt
This prompt chooses a classification threshold based on explicit business objectives rather than the default 0.5 cutoff. It is useful when recall floors, precision targets, or asymmetric costs drive operational decisions. The result makes threshold choice transparent and defensible.
Prompt text
Find the optimal classification threshold for this model given the business context.
1. Generate predicted probabilities for the validation set
2. Evaluate performance across all thresholds from 0.01 to 0.99 (step 0.01):
- Precision, Recall, F1, FPR, TPR at each threshold
3. Plot the threshold vs each metric curve
4. Identify the optimal threshold for three different objectives:
a. Maximize F1-score
b. Maximize precision while keeping recall ≥ {{min_recall}}
c. Minimize total cost given: FP cost = {{fp_cost}}, FN cost = {{fn_cost}}
5. Show the confusion matrix at each of the three optimal thresholds
6. Recommend the final threshold with a business justification
Return: threshold analysis table, metric curves plot, 3 confusion matrices, and final recommendation.When to use this prompt
Use case 02
Business rules impose recall or cost constraints.
Use case 03
You want to compare several threshold objectives side by side.
Use case 04
Stakeholders need confusion matrices at meaningful operating points.
What the AI should return
A threshold performance table, metric-versus-threshold curves, confusion matrices for the selected operating points, and a final threshold recommendation tied to business goals.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Model Evaluation.
Frequently asked questions
What does the Threshold Optimization prompt do?+
It gives you a structured model evaluation starting point for data scientist work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for data scientist workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Threshold Optimization is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are Calibration Analysis, Classification Report, Cross-Validation Deep Dive.