Support "individual"-specific intercept `alpha` in categorical_logit_glm
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- Dominant language
- C++
- Stars
- 839
- Forks
- 220
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 14
Description
The newish categorical_logit_glm function is a great improvement over "manual" softmax regression in terms of efficiency, but I noticed that the current signatures do not allow a case where each "individual" has their own intercept term alpha, i.e. it is an NxM matrix (where N is the number of categories and M is the number of individuals). This is supported in other glm versions such as bernoulli_logit_glm.
Current Version:
v4.3.2
Contributor 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.
Research direction
Start by locating the categorical_logit_glm signatures and compare them with the bernoulli_logit_glm versions that support individual-specific intercepts. Trace the existing categorical_logit_glm implementation and its tests to determine the required NxM alpha behavior. Done means categorical_logit_glm supports an individual-specific alpha matrix consistently with the requested case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100