using yardstick (or other package) for performance calculations
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- machine-learning
Research direction
Review the existing loss-function implementation and compare its coverage with yardstick's metrics, direction metadata, multi-metric support, multiclass metrics, censored regression, and user-defined metrics. The issue names no files, tests, entry points, or completion criteria, so confirm the intended scope and R/Python parity requirements with maintainers before coding.
Written by the indexing model from the issue text.
Description
The loss functions included are great but are somewhat limited.
There are a lot of R packages that could expand the types of loss functions (but at the loss of R:python parity).
If you were to use yardstick, for example, the benefits would be:
- More metrics
- Data on direction (e.g larger-is-better) for each metrics. You wouldn't have to do 1 - AUC anymore.
- yardstick can compute multiple metrics at once.
- Numerous multi-class metrics
- Metrics for censored regression
- Extensible for user-defined metrics
The downside to the current system is that you might optimize your model on a set of performance scores and judge feature importance on some other score.
Let use know if we can help or put in a PR.
- Dominant language
- Python
- Stars
- 1.5k
- Forks
- 172
- PR merge metrics
- No merged PRs in 30d
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.
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