Metadata-Version: 2.3
Name: kernel_quantile_regression
Version: 0.0.3
Summary: Kernel quantile regression
Project-URL: Homepage, https://github.com/luca-pernigo/kernel_quantile_regression
Author-email: Luca Pernigo <luca.pernigo@usi.ch>
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Description-Content-Type: text/markdown

# Kernel quantile regression
The kernel_quantile_regression package is an open source implementation of the quantile regressor techique introduced in  [[1]](#1).



![alt text](https://github.com/luca-pernigo/kernel_quantile_regression/blob/main/plots/melborune_kernel_quantile_regression.png?raw=true)

## Installation
Use the package manager [pip](https://pypi.org/project/kernel-quantile-regression/) to install kernel_quantile_regression.

```bash
pip install kernel-quantile-regression
```

## Usage

```python
from kernel_quantile_regression.kqr import KQR

# create model instance
# specify your quantile q and hyperparameters C and gamma
kqr_1=KQR(alpha=q, C=100, gamma=0.5)

# train model
kqr_1.fit(X_train, y_train)

# predict
kqr_1.predict(X_test)
```

## References
<a id="1">[1]</a> Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, and Alexander J. Smola. 2006. Non-
parametric Quantile Estimation. Journal of Machine Learning Research 7, 45 (2006),
1231–1264. https://www.jmlr.org/papers/volume7/takeuchi06a/takeuchi06a.pdf


## License
[MIT](https://choosealicense.com/licenses/mit/)