JuliaAI / JuliaAI/DecisionTree.jl
Some minor differences in random forest implementations
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 364
- Forks
- 100
- PR merge metrics
- No merged PRs in 30d
Description
I've been comparing some random forest implementations recently (https://github.com/tecosaur/TreeComparison), one of the results of which is #159, but I also have some other information which may be of interest.
For starters, here's the colour coding I use:
Error rates mostly converged among the different implementations I tested, however sometimes ranger does a little bit better:
Precision-recall and ROC curves generally look near-identical, as they should.
I've also noticed some larger differences in the depth and size of the random trees created. Across a number of datasets DecisionTrees.jl and randomForest produce narrower/deeper trees than ranger and sklearn.
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 by reviewing the linked TreeComparison project and related issue #159 to understand the reported comparisons. The issue does not identify a source file, test, reproducible case, or specific behavior to change, so completion criteria would need to be established before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100