Metadata-Version: 2.1
Name: quantile-forest
Version: 1.3.2
Summary: scikit-learn compatible quantile forests.
Home-page: https://zillow.github.io/quantile-forest
Download-URL: https://pypi.org/project/quantile-forest/#files
Maintainer: Zillow Group AI Team
License: Apache License 2.0
Project-URL: Documentation, https://zillow.github.io/quantile-forest
Project-URL: Source, https://github.com/zillow/quantile-forest
Project-URL: Tracker, https://github.com/zillow/quantile-forest/issues
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.23
Requires-Dist: scipy>=1.4
Requires-Dist: scikit-learn>=1.0

# quantile-forest

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**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]](#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 variants.

#### Example of fitted model predictions and prediction intervals on California housing data ([code](https://zillow.github.io/quantile-forest/gallery/plot_quantile_intervals.html))
<img src="https://zillow.github.io/quantile-forest/_static/plot_quantile_intervals.png"/>

Quick Start
-----------

Install quantile-forest from [PyPI](https://pypi.org/project/quantile-forest) using `pip`:

```bash
pip install quantile-forest
```

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](https://zillow.github.io/quantile-forest).


References
----------

<a id="1">[1]</a> 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}
}
```
