JuliaAI / JuliaAI/DecisionTree.jl
TODO: Out of bag error
Open
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 364
- Forks
- 100
- PR merge metrics
- No merged PRs in 30d
Description
It would be awesome if we could compute the out of bag error in the process of training the random forest. We can naturally do a sort of cross validation in random forests by testing on the data that we leave out in the process of training each tree through bagging.
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
No files, tests, or entry points are named. Start by locating the random-forest training and bagging implementation, then determine how held-out samples are tracked; done means computing and exposing the out-of-bag error during training.
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
- Needs clarification
- Newbie friendliness
- 25/100