The-Strategy-Unit / The-Strategy-Unit/data_science
“traditional” ML techniques vs AI for explainability and reproducibility:
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 11
- Forks
- 5
- Avg merge
- 5h 34m
- Merged PRs (30d)
- 1
Description
a session about the tradeoffs: learn hard thing, gain skills and understand what you're doing/what the outputs mean, versus much greater user simplicity but potentially much lower certainty in results (without necessarily being able to even quantify the error)
Contributor guide
No contributing guide indexed for this repository
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 with the issue body and clarify the intended session scope around traditional machine-learning techniques, AI, explainability, and reproducibility. There are no files or tests named; done would mean an agreed session plan that clearly presents the tradeoffs and expected learning outcomes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100