JuliaAI / JuliaAI/DecisionTree.jl
Obscenely slow prediction
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- Dominant language
- Julia
- Stars
- 364
- Forks
- 100
- PR merge metrics
- No merged PRs in 30d
Description
Hello,
I'd love to use DecisionTree.jl for a project I'm currently working on, as it's great in lot of ways. Speedy to train, players nicely with AbstractTrees, etc.
Unfortunately, saying the prediction performance is "not good" is putting things mildly. I did a test run with an simplified version of one of the data sets I'm working with, and recorded the training and prediction times of DecisionTree.jl as well as a number of other common random forest implementations.
| Tool | Train time | Predict time | Ratio |
|---|---|---|---|
| DecisionTree.jl | 0.6s | 175s | 292 |
| randomForest | 24.4s | 4.2s | 0.17 |
| ranger | 1.9s | 0.5s | 0.26 |
| sklearn | 63s | 1.7s | 0.03 |
The competitiveness of the training time gives me hope that the DecisionTrees.jl should be able to be competitive with prediction performance too 🙂.
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
Reproduce the prediction benchmark described in the issue and compare it with the listed random-forest implementations. Profile the DecisionTree.jl prediction path to identify the source of the slowdown; done means prediction performance is substantially improved and the benchmark results are documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100