Data ScientistModel BuildingAdvancedSingle prompt

Custom Loss Function AI Prompt

This prompt builds a model objective around business cost instead of default statistical loss. It is useful when false positives and false negatives have very different consequences, such as fraud, medical screening, or retention interventions. The output translates model quality into financial terms.

Prompt text
Implement a custom loss function for this problem that better reflects the business cost of different types of errors.

Business context: {{business_context}}
Cost structure:
- False positive cost: {{fp_cost}} (e.g. unnecessary intervention costs $10)
- False negative cost: {{fn_cost}} (e.g. missed fraud costs $500)

1. Define the asymmetric cost matrix
2. Implement a custom objective function for LightGBM/XGBoost that minimizes expected business cost
3. Implement a custom evaluation metric that reports cost in business units
4. Train the model with the custom loss and compare to cross-entropy loss:
   - Standard accuracy / AUC / F1
   - Business cost per 1000 predictions
   - Optimal decision threshold under the cost structure
5. Show the threshold vs business cost curve — at what threshold is business cost minimized?

Return the custom loss code and the business cost comparison table.

When to use this prompt

Use case 01

Standard metrics do not reflect the true business cost of errors.

Use case 02

False positives and false negatives have asymmetric impact.

Use case 03

You want to optimize decision thresholds based on business value, not only AUC.

Use case 04

The project needs a custom objective for boosting models.

What the AI should return

Custom loss and evaluation code, a business-cost comparison between standard and custom training, threshold-versus-cost analysis, and a recommendation for the cost-minimizing operating point.

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 Building.

Frequently asked questions

What does the Custom Loss Function prompt do?+

It gives you a structured model building 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 advanced, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Custom Loss Function 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 AutoML Benchmark, Baseline Model, Class Imbalance Handling.