{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/18"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"}],"enrichment":{"capability":"Quantile regression forests for estimating conditional quantiles and prediction intervals, with scikit-learn compatibility and Cython optimization.","skillfed_tags":["quantile-regression","uncertainty-quantification","ensemble-methods"],"use_cases":["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."],"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.\n\nThe 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\u20133.14 on macOS, Windows, and Linux, with dependencies on numpy, scipy, and scikit-learn.","worth_installing":"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."},"id":"quantile-forest","links":{"html":"https://skillfed.io/packages/quantile-forest","md":"https://skillfed.io/packages/quantile-forest.md","pypi":"https://pypi.org/project/quantile-forest/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-21","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"quantile-forest","python_support":"supports_current","summary":"Quantile regression forests compatible with scikit-learn."},"popularity":{"monthly_downloads":78362,"position":14448,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.2"}