JuliaAI / JuliaAI/DataScienceTutorials.jl
Tuning using RandomSearch
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- Dominant language
- ReScript
- Stars
- 126
- Forks
- 19
- PR merge metrics
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Description
Be good to add this. Very basic example is in the manual:
https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/#Tuning-using-a-random-search-1
which also includes the detailed doc-string. The doc-string for StatsBase.fit there also has relevant information about how various distributions get fit to a range if an explicit prior is not specified.
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
Read the linked MLJ manual section on tuning with a random search, including the referenced StatsBase.fit doc-string. Use those examples to identify the relevant tutorial content and add a basic random-search tuning example; the work is done when the tutorial covers the example and the documented prior-fitting behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100