Audit benchmarking configuration scripts
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- Dominant language
- Python
- Stars
- 107
- Forks
- 46
- PR merge metrics
- No merged PRs in 30d
Description
We need to take a look through the default configuration and ensure that each benchmarking algorithm has more than one library and a feasible (and sensible) set of datasets and options to go with it.
I will handle this one as I am able to.
This is in reference to mlpack/mlpack#1350.
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
Start by locating the default benchmarking configuration and the definitions for each algorithm. Review the available libraries, datasets, and options for every algorithm; the audit is done when each has more than one library and feasible, sensible combinations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100