Data ScientistModel EvaluationBeginnerSingle prompt

Regression Evaluation AI Prompt

This prompt evaluates regression models from several complementary angles. It is useful for checking raw accuracy, residual structure, bias patterns, and specific failure cases. The aim is to understand not only how wrong the model is, but where and why.

Prompt text
Evaluate this regression model comprehensively.

1. Compute: MAE, RMSE, MAPE, R², and Adjusted R²
2. Plot predicted vs actual values — how close are points to the diagonal?
3. Plot residuals vs predicted values — check for patterns (heteroscedasticity, non-linearity)
4. Plot residual distribution — should be approximately normal with mean near zero
5. Identify the top 10 largest errors (by absolute residual) — do they share any characteristics?
6. Check for systematic bias: does the model over-predict or under-predict for certain segments?

Return: metric table, 4 diagnostic plots, a table of worst predictions with row details, and a one-paragraph model assessment.

When to use this prompt

Use case 01

You are validating a regression model before trusting it.

Use case 02

You want residual diagnostics and not just RMSE or MAE.

Use case 03

You need to inspect the worst predictions in detail.

Use case 04

You want to identify segment-specific bias or heteroscedasticity.

What the AI should return

A regression metric table, four diagnostic plots, a worst-error table with row-level detail, and a concise written assessment of model quality and likely issues.

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 Model Evaluation.

Frequently asked questions

What does the Regression Evaluation prompt do?+

It gives you a structured model evaluation 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?+

Regression Evaluation 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 Calibration Analysis, Classification Report, Cross-Validation Deep Dive.