JuliaAI / JuliaAI/DecisionTree.jl
force/auto-convert to one-hot encoding for categorical features
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 364
- Forks
- 100
- PR merge metrics
- No merged PRs in 30d
Description
The current implementation uses the lexicographical ordering to calculate splits of string features. But in practice, this is rarely intended since categorical features are by definition unordered (for example, it wouldn't make any sense that "Blue" < "Red" < "Yellow".) One hot encoding would decouple the categorical variable from any unintended ordering, and allow, as is not currently the case, regression on datasets with categorical features.
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
No files, tests, or entry points are named. Start by locating the categorical split handling and the regression path, then verify that categorical features no longer depend on lexicographical ordering and that regression accepts datasets containing them.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100