JuliaAI / JuliaAI/DecisionTree.jl
categorical features handled "correctly"?
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 364
- Forks
- 100
- PR merge metrics
- No merged PRs in 30d
Description
Does this package "correctly" handle categorical variables (e.g. without conversion to numerical encoding schemes like one-hot or ordinal encoding), as that ability is a distinct advantage of decision trees and their progeny? Issues #61 and #13 are related but it is not clear to me what the current status is. Perhaps if they are supported, I could make a documentation PR for a brief mention on the README.
If so, it would be a good reason for some users to switch from scikit-learn's RF implementation, which still requires numerical encoding.
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 reading related issues #61 and #13, then inspect the README and the package's current handling of categorical variables. Confirm whether categorical features work without numerical encoding and document the verified behavior in the README if appropriate.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100