Quality of Surrogate Models
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- Dominant language
- Python
- Stars
- 104
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
Many of our analyses are based on surrogate models.
It would be fairly important to know how faithful these surrogate models actually are.
Could we add some insights regarding that? In the easiest case, we could start with some RMSE on out-of-bag error.
Contributor guide
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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.
Research direction
Start by locating the surrogate-model analysis and the existing visualization or reporting entry points in DeepCAVE. Review how surrogate models are produced and what out-of-bag information is already available, then clarify which faithfulness insights and RMSE presentation are expected. Done means the requested quality information is defined, exposed to users, and covered by appropriate tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100