Data ScientistModel EvaluationIntermediateSingle prompt

Calibration Analysis AI Prompt

This prompt checks whether predicted probabilities can be trusted as probabilities, not just rankings. It is useful for decision systems that depend on calibrated risk estimates, thresholds, or expected value calculations. The workflow compares raw and calibrated models with proper holdout discipline.

Prompt text
Assess and improve the probability calibration of this classification model.

1. Plot a reliability diagram (calibration curve): predicted probability vs actual fraction of positives, using 10 bins
2. Compute the Expected Calibration Error (ECE) and Maximum Calibration Error (MCE)
3. Determine if the model is overconfident (predictions too extreme) or underconfident (predictions too moderate)
4. Apply two calibration methods and compare:
   a. Platt Scaling (logistic regression on model outputs)
   b. Isotonic Regression
5. Plot calibration curves before and after each method
6. Report ECE before and after calibration

Note: calibration must be fitted on a held-out calibration set (not the training set) to avoid overfitting.

When to use this prompt

Use case 01

You care about probability quality, not only classification ranking.

Use case 02

The model will be used for thresholding, triage, or expected-cost decisions.

Use case 03

You want to compare Platt scaling and isotonic regression properly.

Use case 04

You need reliability diagrams and calibration error metrics.

What the AI should return

Calibration plots before and after adjustment, ECE and MCE metrics, a statement about overconfidence or underconfidence, and a recommendation on whether calibration should be applied in production.

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 Calibration Analysis 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?+

Calibration Analysis 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 Classification Report, Cross-Validation Deep Dive, Drift Detection.