[Feature Request] Confidence bands for polynomial and nonlinear regression
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- fsharp
- Domain
- data
Research direction
Start by locating the existing confidence and prediction band implementation for simple linear regression, then review the linked Scheffé method and regression references. Done means providing a defined generalized approach for linear and nonlinear regression, with confidence and prediction bands and corresponding validation.
Written by the indexing model from the issue text.
Description
Is your feature request related to a problem? Please describe.
Confidence and prediction bands offer a interesting insight into regression outcomes. While the fitted function (correct model selection is assumed) shows the best possibility of the current model, it does not show the confidence.
While confidence and prediction bands are implemented for simple linear regression, a generalized version for linear regression and non-linear regression would be beneficial.
Additional context
- Scheffés method
- https://www.graphpad.com/guides/prism/7/curve-fitting/reg_confidence_and_prediction_band.htm
- https://www.graphpad.com/guides/prism/7/curve-fitting/reg_how_confidence_and_prediction_.htm
- https://nbviewer.org/github/gpeyre/numerical-tours/blob/master/python/ml_11_conformal_prediction.ipynb
- Dominant language
- F#
- Stars
- 227
- Forks
- 58
- Avg merge
- 2d 7h
- Merged PRs (30d)
- 1
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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