Data ScientistFeature EngineeringIntermediateSingle prompt

Interaction Features AI Prompt

This prompt searches for pairwise feature interactions that add predictive value beyond the original variables. It is useful when the target may depend on combinations, contrasts, or ratios rather than single features alone. The output focuses on interactions that are both meaningful and not excessively redundant.

Prompt text
Generate and evaluate interaction features between the most important variables in this dataset.

1. Identify the top 6 numeric features by correlation with {{target_variable}}
2. Create all pairwise interactions between them:
   - Multiplication: feature_a × feature_b
   - Ratio: feature_a / (feature_b + epsilon)
   - Difference: feature_a - feature_b
3. For each interaction feature, compute its correlation with {{target_variable}}
4. Keep only interaction features with |r| > 0.05 with the target and that outperform their parent features
5. Check for multicollinearity between interaction features and parents

Return the top 10 interaction features ranked by correlation with the target, with code to create them.

When to use this prompt

Use case 01

You suspect interactions matter more than single raw variables.

Use case 02

You want to test multiplicative, ratio, and difference features systematically.

Use case 03

You need a quick way to rank interactions by relationship to the target.

Use case 04

You want to avoid keeping noisy interactions that only add multicollinearity.

What the AI should return

A ranked shortlist of the top interaction features, their target correlations, screening logic used to keep or discard them, multicollinearity notes, and code to recreate the selected interactions.

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 Feature Engineering.

Frequently asked questions

What does the Interaction Features prompt do?+

It gives you a structured feature engineering 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?+

Interaction Features 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 Date Feature Extraction, Embedding Features from Text, Feature Ideas Generator.