quantile-forest
Quantile regression forests compatible with scikit-learn.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive Apache-2.0 license, and offers a compatible API for quantile regression. Medium install friction is offset by prebuilt wheels and straightforward dependencies on numpy, scipy, and scikit-learn. Suitable for production use in uncertainty quantification and prediction interval tasks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; numpy, scipy, and scikit-learn must be installed.
- Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.9–3.14 across macOS, Windows, and Linux, reducing build complexity.
- Active maintenance with a recent release 54 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-06-21 (54 days) · last repo commit 2026-08-10 · 254 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 78,362 downloads/mo, #14,448 on PyPI
Alternatives
Verify before relying
pip install quantile-forest
from quantile_forest import RandomForestQuantileRegressor
import numpy
qrf = RandomForestQuantileRegressor()
qrf.fit(X, y)
y_pred = qrf.predict(X, quantiles=[0.025, 0.5, 0.975])- Performance benchmarks against other quantile regression implementations.
- Scalability limits on dataset size or dimensionality for practical applications.
- Whether out-of-bag estimation and proximity count methods are documented with usage examples.
What it is and what it does
Quantile-forest is a Python package that implements quantile regression forests (QRF), a non-parametric tree-based ensemble method for estimating conditional quantiles. It extends forest estimators with the ability to predict arbitrary quantiles at inference time without retraining, making it useful for uncertainty quantification and prediction interval estimation on high-dimensional data.
The package provides Cython-optimized estimators that are compatible with and can serve as drop-in replacements for forest regressors. It supports out-of-bag estimation, quantile rank calculation, and proximity counting. Prebuilt wheels are available for Python 3.9–3.14 on macOS, Windows, and Linux, with dependencies on numpy, scipy, and scikit-learn.
Use it for
- Estimate prediction intervals for regression tasks to quantify uncertainty around point predictions.
- Build uncertainty-aware models for high-dimensional data where conditional quantile estimates are needed.
- Add quantile prediction to existing pipelines without retraining or major refactoring.
- Perform out-of-bag quantile estimation for model validation and robustness assessment.
- Compute quantile ranks and proximity counts for ensemble diagnostics and model interpretability.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, uses a permissive Apache-2.0 license, and offers a compatible API for quantile regression. Medium install friction is offset by prebuilt wheels and straightforward dependencies on numpy, scipy, and scikit-learn. Suitable for production use in uncertainty quantification and prediction interval tasks.
Install
quantile-forest on PyPI
Before you install
Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.9–3.14 across macOS, Windows, and Linux, reducing build complexity. Active maintenance with a recent release 54 days ago.
Requires Python 3.9 or later; numpy, scipy, and scikit-learn must be installed.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install quantile-forest
from quantile_forest import RandomForestQuantileRegressor
import numpy
qrf = RandomForestQuantileRegressor()
qrf.fit(X, y)
y_pred = qrf.predict(X, quantiles=[0.025, 0.5, 0.975])Verify before relying
- Performance benchmarks against other quantile regression implementations.
- Scalability limits on dataset size or dimensionality for practical applications.
- Whether out-of-bag estimation and proximity count methods are documented with usage examples.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnumpyscipyscikit-learn |
| Maintenance | Actively maintained 54 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 78,362 / month, #14,448 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/EngineeringTopic :: Software Development |
Evidence: quantile_forest-1.4.2-cp310-cp310-macosx_10_9_universal2.whl; quantile_forest-1.4.2-cp310-cp310-macosx_10_9_x86_64.whl; quantile_forest-1.4.2-cp310-cp310-macosx_11_0_arm64.whl; quantile_forest-1.4.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; quantile_forest-1.4.2-cp310-cp310-win_amd64.whl; quantile_forest-1.4.2-cp311-cp311-macosx_10_9_universal2.whl; quantile_forest-1.4.2-cp311-cp311-macosx_10_9_x86_64.whl; quantile_forest-1.4.2-cp311-cp311-macosx_11_0_arm64.whl; quantile_forest-1.4.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; quantile_forest-1.4.2-cp311-cp311-win_amd64.whl; quantile_forest-1.4.2-cp312-cp312-macosx_10_13_universal2.whl; quantile_forest-1.4.2-cp312-cp312-macosx_10_13_x86_64.whl; quantile_forest-1.4.2-cp312-cp312-macosx_11_0_arm64.whl; quantile_forest-1.4.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; quantile_forest-1.4.2-cp312-cp312-win_amd64.whl; quantile_forest-1.4.2-cp313-cp313-macosx_10_13_universal2.whl; quantile_forest-1.4.2-cp313-cp313-macosx_10_13_x86_64.whl; quantile_forest-1.4.2-cp313-cp313-macosx_11_0_arm64.whl; quantile_forest-1.4.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; quantile_forest-1.4.2-cp313-cp313-win_amd64.whl
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