Systematically determine `min_dist` and `n_neighbors`
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Description
I tested multiple combination of min_dist and n_neighbor on my data, I found that a suitable combination of those hyperparameters can separate the all the cluster(according to labels). I wonder how to determine those hyperparameters systematically, not by visualization of the data after embedding.
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
The issue names no file, test, or entry point. Start by tracing how UMAP exposes and uses min_dist and n_neighbors, then determine what a systematic selection method would need to measure. Done is not defined in the issue, so an acceptance criterion for the method would need to be established first.
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