Data ScientistExplainabilityBeginnerSingle prompt

Feature Importance AI Prompt

This prompt provides a straightforward explanation of what features matter most and whether different importance methods agree. It is a practical first step in explainability for models that support built-in importances or can be probed with permutation tests. It also helps identify candidates for feature pruning.

Prompt text
Explain which features matter most to this model.

1. Extract built-in feature importances from the model (gain, split count, or permutation importance)
2. Plot a horizontal bar chart of the top 20 features, ranked by importance
3. Compute permutation importance on the validation set as a cross-check — compare to built-in importances
4. Flag any features where built-in and permutation importances disagree significantly
5. Identify features with near-zero importance in both methods — candidates for removal
6. Group features by type (original vs engineered) and show which group contributes more total importance

Return: importance table, bar chart, and a one-paragraph plain-English explanation of what the model is learning.

When to use this prompt

Use case 01

You need a quick interpretable summary of model drivers.

Use case 02

You want to compare built-in importance with permutation importance.

Use case 03

You suspect some engineered features may be doing little work.

Use case 04

You need a plain-language explanation for stakeholders.

What the AI should return

An importance table, ranked feature plot, disagreement flags between methods, near-zero-importance candidates, and a plain-English summary of what the model appears to learn.

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 Feature Importance 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 beginner, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Feature Importance 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, Full XAI Chain.