quantile-forest
quantile-forest: A Python Package for Quantile Regression Forests - Published in JOSS (2024)
Science Score: 93.0%
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Found 8 DOI reference(s) in README and JOSS metadata -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Scientific Fields
Repository
Quantile Regression Forests compatible with scikit-learn.
Basic Info
- Host: GitHub
- Owner: zillow
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://zillow.github.io/quantile-forest/
- Size: 81.8 MB
Statistics
- Stars: 241
- Watchers: 10
- Forks: 30
- Open Issues: 2
- Releases: 26
Topics
Metadata Files
README.md
quantile-forest 
quantile-forest offers a Python implementation of quantile regression forests compatible with scikit-learn.
Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation [1]. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn forest regressors.
Example of fitted model predictions and prediction intervals on California housing data (code)
Quick Start
To install quantile-forest from PyPI using pip:
bash
pip install quantile-forest
To install quantile-forest from conda-forge using conda:
bash
conda install quantile-forest -c conda-forge
Usage
python
from quantile_forest import RandomForestQuantileRegressor
from sklearn import datasets
X, y = datasets.fetch_california_housing(return_X_y=True)
qrf = RandomForestQuantileRegressor()
qrf.fit(X, y)
y_pred = qrf.predict(X, quantiles=[0.025, 0.5, 0.975])
Documentation
An installation guide, API documentation, and examples can be found in the documentation.
References
[1] N. Meinshausen, "Quantile Regression Forests", Journal of Machine Learning Research, 7(Jun), 983-999, 2006. http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf
Citation
If you use this package in academic work, please consider citing https://joss.theoj.org/papers/10.21105/joss.05976:
bib
@article{Johnson2024,
doi = {10.21105/joss.05976},
url = {https://doi.org/10.21105/joss.05976},
year = {2024},
publisher = {The Open Journal},
volume = {9},
number = {93},
pages = {5976},
author = {Reid A. Johnson},
title = {quantile-forest: A Python Package for Quantile Regression Forests},
journal = {Journal of Open Source Software}
}
Owner
- Name: Zillow
- Login: zillow
- Kind: organization
- Location: United States
- Website: https://zillowgroup.com/
- Repositories: 147
- Profile: https://github.com/zillow
JOSS Publication
quantile-forest: A Python Package for Quantile Regression Forests
Authors
Zillow Group, USA
Editor
Mehmet Hakan Satman
Tags
Machine Learning Quantile RegressionGitHub Events
Total
- Create event: 21
- Release event: 1
- Issues event: 2
- Watch event: 30
- Delete event: 20
- Issue comment event: 9
- Push event: 49
- Pull request event: 37
- Fork event: 5
Last Year
- Create event: 21
- Release event: 1
- Issues event: 2
- Watch event: 30
- Delete event: 20
- Issue comment event: 9
- Push event: 49
- Pull request event: 37
- Fork event: 5
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Reid Johnson | r****j@z****m | 342 |
| dependabot[bot] | 4****] | 32 |
| CarpeFridiem | b****1@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 22
- Total pull requests: 158
- Average time to close issues: 9 days
- Average time to close pull requests: 3 days
- Total issue authors: 19
- Total pull request authors: 3
- Average comments per issue: 2.86
- Average comments per pull request: 0.59
- Merged pull requests: 138
- Bot issues: 1
- Bot pull requests: 69
Past Year
- Issues: 3
- Pull requests: 60
- Average time to close issues: 1 day
- Average time to close pull requests: 7 days
- Issue authors: 3
- Pull request authors: 3
- Average comments per issue: 2.67
- Average comments per pull request: 0.38
- Merged pull requests: 47
- Bot issues: 0
- Bot pull requests: 40
Top Authors
Issue Authors
- felix7602 (3)
- brentonmallen1 (2)
- w1ll1a9m (1)
- jncraton (1)
- yzhang3000 (1)
- TamaraAtanasoska (1)
- HuashiSCNU0303 (1)
- dependabot[bot] (1)
- qinhaohcen (1)
- vascomedici (1)
- adrinjalali (1)
- t-reents (1)
- jmaddalena (1)
- brdeleeuw (1)
- ogrisel (1)
Pull Request Authors
- reidjohnson (88)
- dependabot[bot] (69)
- brentonmallen1 (1)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 20,880 last-month
- Total dependent packages: 2
- Total dependent repositories: 2
- Total versions: 26
- Total maintainers: 2
pypi.org: quantile-forest
Quantile regression forests compatible with scikit-learn.
- Homepage: https://zillow.github.io/quantile-forest
- Documentation: https://zillow.github.io/quantile-forest
- License: Apache License 2.0
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Latest release: 1.4.0
published 11 months ago
Rankings
Maintainers (2)
Dependencies
- matplotlib *
- numpydoc *
- sphinx *
- sphinx_gallery *
- sphinx_rtd_theme *
- sphinxcontrib.bibtex *
- cython >=3.0a4
- numpy *
- scikit-learn >=1.0
- scipy *