Data ScientistFeature EngineeringAdvancedSingle prompt

Polynomial and Spline Features AI Prompt

This prompt adds polynomial and spline transformations to model non-linear feature effects explicitly. It is useful when simple linear representations miss curvature, thresholds, or diminishing returns in the relationship with the target. The workflow evaluates whether these richer forms actually improve cross-validated performance.

Prompt text
Create polynomial and spline features to capture non-linear relationships in this dataset.

1. Identify the top 5 numeric features by correlation with {{target_variable}}
2. For each, test whether the relationship is linear, quadratic, or higher-order:
   - Fit linear, quadratic, and cubic regression
   - Compare R² values and plot each fit
3. For features with non-linear relationships:
   a. Add polynomial features (degree 2 and 3)
   b. Add natural cubic spline features with 4 knots at the 25th, 50th, 75th, and 90th percentiles
4. Add the polynomial/spline features to the model and compare:
   - CV score before adding
   - CV score after adding
   - Risk of overfitting (train vs val gap)
5. Use SHAP to verify the model is using the polynomial features meaningfully

Return: relationship type table, feature code, and CV performance comparison.

When to use this prompt

Use case 01

Important numeric features may have curved rather than linear effects.

Use case 02

You want to compare linear, quadratic, cubic, and spline fits systematically.

Use case 03

You need feature code plus evidence that the new terms help.

Use case 04

You want to monitor overfitting while expanding the feature space.

What the AI should return

A table describing the relationship shape for each tested feature, code for selected polynomial and spline terms, and CV performance comparison before and after adding them.

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 Polynomial and Spline 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 advanced, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Polynomial and Spline 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.