Hyperparameter Marginalization
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 6
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
We need a method to define a population model with uncertainty. This can be in an abstract sense or in a more specific sense. For example, just marginalizing over the class prior could be very useful, and would be much more tractable than marginalizing hyperparameters related to the shape of the population predictions in the observable parameter space.
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
The issue names no files, tests, or entry points. Start by reviewing the package's population-model and classification interfaces, then determine whether class-prior marginalization is the initial scope. Done should include an agreed API and tests for the selected marginalization behavior.
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
- 20/100