Data ScientistExplainabilityIntermediateSingle prompt

SHAP Analysis AI Prompt

This prompt uses SHAP to explain both global model behavior and individual predictions. It is useful when you need richer insight than a single importance ranking, including effect direction and case-level reasoning. It is one of the strongest all-around explainability prompts for tabular models.

Prompt text
Generate a complete SHAP-based model explanation.

1. Compute SHAP values for all predictions in the validation set
2. Global explanations:
   - Beeswarm plot: feature importance + direction of effect
   - Bar plot: mean absolute SHAP value per feature (top 20)
3. Dependence plots for the top 3 most important features:
   - SHAP value on y-axis, feature value on x-axis
   - Color by the most important interaction feature
4. Local explanations — waterfall plots for:
   - The most confidently correct prediction
   - The most confidently wrong prediction
   - One typical prediction near the decision boundary
5. Plain-English summary: what are the top 3 drivers of high predictions vs low predictions?

Return all plots and the plain-English summary.

When to use this prompt

Use case 01

You need both global and local interpretability.

Use case 02

Stakeholders want to know why specific predictions were made.

Use case 03

You want direction-of-effect plots rather than just rankings.

Use case 04

The model is complex enough that standard importances are not enough.

What the AI should return

SHAP beeswarm and bar plots, dependence plots, selected local waterfall explanations, and a plain-English summary of the main factors that drive high versus low predictions.

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 Explainability.

Frequently asked questions

What does the SHAP Analysis prompt do?+

It gives you a structured explainability 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?+

SHAP 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 Counterfactual Explanations, Decision Tree Proxy, Feature Importance.