szilard / GBM-perf
Performance of various open source GBM implementations
Why this job: works in the areas we cover, but does not say which job it does
What it does
Performance of the top/most widely used open source gradient boosting machines (GBM)/ boosted trees (GBDT) implementations (h2o, xgboost, lightgbm, catboost) on the airline dataset (100K, 1M and 10M records) and with 100 trees, depth 10, learning rate 0.1.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 8 months ago
- Stars
- 225
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- HTML
- 99% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 38% closed
- 36 still open
- Pull requests
- 3 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- HTML 99%
- R <1%
- Python <1%
- Dockerfile <1%
Topics
- machine-learning
- gradient-boosting-machine
- gbm
- h2oai
- xgboost
- lightgbm
- benchmark
Project record
- JobOther AI tools
- StatusListed
- Commits520
- Forks30
- Watching20
- Stars in 28 days0
- CreatedMay 27, 2017
- First seen by usNov 7, 2026
- CheckedNov 8, 2026