scikit-learn / scikit-learn/scikit-learn
Add subsample and max_features parameters to HistGradientBoostingRegressor
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module:ensemble
New Feature
- Dominant language
- Python
- Stars
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- Avg merge
- 1d 15h
- Merged PRs (30d)
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Description
The parameters subsample and max_features in GradientBoostingRegressor are useful. Is it possible to add equivalent parameters to HistGradientBoostingRegressor?
Contributor guide
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 comparing the named GradientBoostingRegressor and HistGradientBoostingRegressor entry points, focusing on how subsample and max_features are defined and behave in the existing estimator. Done means HistGradientBoostingRegressor supports equivalent parameters with documented, validated behavior and coverage for the relevant cases.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100