Data ScientistExplainabilityIntermediateSingle prompt

Partial Dependence Plots AI Prompt

This prompt maps how changing a feature affects the model on average and across individual cases. It is useful for identifying monotonic, threshold, or highly heterogeneous feature effects. The combination of PDP and ICE helps distinguish average behavior from person-level variability.

Prompt text
Generate partial dependence plots (PDPs) and individual conditional expectation (ICE) plots for the top features in this model.

For each of the top 5 most important features:
1. Plot the PDP: how does the average model prediction change as this feature varies across its range?
2. Overlay 50 randomly sampled ICE curves to show individual variation around the average
3. Highlight the average ICE curve in bold
4. Mark the actual data distribution (rug plot) on the x-axis to show where data is sparse
5. Describe the relationship: monotonic increasing, monotonic decreasing, non-linear, threshold effect?

Also create one 2D PDP for the top pair of interacting features (identified from SHAP interaction values).

Return all plots and a table summarizing the relationship type for each feature.

When to use this prompt

Use case 01

You want to understand the functional form of key feature effects.

Use case 02

A feature may have threshold or non-linear behavior.

Use case 03

You need to see whether average effects hide strong individual variation.

Use case 04

You want to inspect one important pairwise interaction visually.

What the AI should return

PDP and ICE plots for top features, one 2D interaction plot, and a summary table describing each feature's relationship type and any evidence of heterogeneity.

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 Partial Dependence Plots 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?+

Partial Dependence Plots 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.