Synthetic regression data with declared coefficients and split
Generate 128 regression observations with known coefficients, a declared Gaussian noise process, and a fixed train/test split, together with a small reference fit.
Proposed original example. Source release and any execution are separate steps.
Can another implementation recover the reference fit and evaluate predictions without changing the test split?
Preserve all generated rows, known population parameters, the closed-form training fit, and held-out errors; sample estimates are not expected to equal generator parameters exactly.
What you could produce
- A scoped observations JSON record and a comparison with the stated reference.
Before you use it
- Python 3.13 standard library
Limits to keep in view
- No research code was executed by the preparation tool.
- Author output cannot issue an independent scientific-verification result.
Source and permission context
Original local preparation by the Executable Science seed collection; upstream API references remain separately attributed.
Catalog listing reviewed. This review covers the description and source links displayed here.
Local draft title, collective byline, original summary, and proposed split terms reviewed.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
Before copying source material
- Proposed local-draft licenses: original code MIT, explanations CC-BY-4.0, synthetic numeric data CC0-1.0; publication/disclosure approval remains separate.