[Feature Request] Hyper Parameter Tuning
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- fsharp
- Domain
- machine-learning
Research direction
Start by reviewing the existing machine-learning algorithms and project structure in FSharp.Stats to identify suitable extension points for hyperparameter tuning. Define the intended framework scope, supported tuning algorithms, and completion criteria before implementation; the issue does not name specific files, tests, or an entry point.
Written by the indexing model from the issue text.
Description
Is your feature request related to a problem? Please describe.
Many Machine learning algorithms take additional hyper parameters which can affect the model performance quite a bit. Finding the best hyper parameter values is called hyper parameter tuning (HPT). There are several approaches to do this.
Describe the solution you'd like
IMO having some kind of HPT framework and some HPT algorithms could well complement the set of machine learning algorithms implemented in FSharp.Stats.
- 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.
More from fslaborg/FSharp.Stats
-
Documentation
Difficulty 2/5 1-3 hours Newbie friendliness 72/100
fslaborg/FSharp.Stats#385 · 1 comment ·
-
agentic-workflows
Difficulty 3/5 1-2 days Newbie friendliness 35/100
fslaborg/FSharp.Stats#393 ·
-
agentic-workflows
Difficulty 4/5 3-5 days Newbie friendliness 45/100
fslaborg/FSharp.Stats#391 ·
-
automation Documentation repo-assist
Difficulty 4/5 3-5 days Newbie friendliness 20/100
fslaborg/FSharp.Stats#390 ·
-
automation Documentation repo-assist
Difficulty 1/5 Under an hour Newbie friendliness 1/100
fslaborg/FSharp.Stats#382 ·
All issues in fslaborg/FSharp.Stats
Similar issues
-
`qwenMTPSanitizeWeights` cannot load a standalone published MTP headPossibly taken @aleroot claimed this today. Open
Difficulty 2/5 1-3 hours Newbie friendliness 78/100
ml-explore/mlx-swift-lm#678 · 1 comment ·
Maintainers usually reply within 1 day
-
An all-zero continuing mass now scores the first token as certain, so a candidate the model gave no probability can passPossibly taken @naveen-bhatt claimed this 1 day ago. Openarea:ai-suggestions bug P2
Difficulty 2/5 1-3 hours Newbie friendliness 84/100
uttrflow/uttrflow-swift#5115 ·
Maintainers usually reply within 1 day
-
Zero-token evaluations are treated as missing cost in selectionPossibly taken @sylvesterkaczmarek claimed this 1 day ago. Open
Difficulty 2/5 1-3 hours Newbie friendliness 76/100
google-research/rrsi#6 ·
-
SAC actor crashes in training (train.py) : TanhNormalProjectionNetwork.call() got unexpected network_statePossibly taken A pull request linked to this issue is open or already merged. Open
Difficulty 1/5 Under an hour Newbie friendliness 85/100
-
area/evaluation bug
Difficulty 2/5 1-3 hours Newbie friendliness 88/100
Maintainers usually reply within 1 day