Supervised learning for probabilistic prediction
Project description
<p align="center">
<a href="https://badge.fury.io/py/skpro"><img src="https://badge.fury.io/py/skpro.svg" alt="PyPI version" height="18"></a>
<a href="https://travis-ci.org/alan-turing-institute/skpro"><img src="https://travis-ci.org/alan-turing-institute/skpro.svg?branch=master" alt="Build Status"></a>
<a href="https://opensource.org/licenses/BSD-3-Clause"><img src="https://img.shields.io/badge/License-BSD%203--Clause-blue.svg" alt="License"></a>
</p>
A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points.
The package offers a variety of features and specifically allows for
- the implementation of probabilistic prediction strategies in the supervised contexts
- comparison of frequentist and Bayesian prediction methods
- strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging)
- workflow automation
List of [developers and contributors](AUTHORS.rst)
### Documentation
The full documentation is [available here](https://alan-turing-institute.github.io/skpro/).
### Installation
Installation is easy using Python's package manager
$ pip install skpro
### Contributing & Citation
We welcome contributions to the skpro project. Please read our [contribution guide](/CONTRIBUTING.md).
If you use skpro in a scientific publication, we would appreciate [citations](CITATION.rst).
<p align="center">
<a href="https://badge.fury.io/py/skpro"><img src="https://badge.fury.io/py/skpro.svg" alt="PyPI version" height="18"></a>
<a href="https://travis-ci.org/alan-turing-institute/skpro"><img src="https://travis-ci.org/alan-turing-institute/skpro.svg?branch=master" alt="Build Status"></a>
<a href="https://opensource.org/licenses/BSD-3-Clause"><img src="https://img.shields.io/badge/License-BSD%203--Clause-blue.svg" alt="License"></a>
</p>
A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points.
The package offers a variety of features and specifically allows for
- the implementation of probabilistic prediction strategies in the supervised contexts
- comparison of frequentist and Bayesian prediction methods
- strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging)
- workflow automation
List of [developers and contributors](AUTHORS.rst)
### Documentation
The full documentation is [available here](https://alan-turing-institute.github.io/skpro/).
### Installation
Installation is easy using Python's package manager
$ pip install skpro
### Contributing & Citation
We welcome contributions to the skpro project. Please read our [contribution guide](/CONTRIBUTING.md).
If you use skpro in a scientific publication, we would appreciate [citations](CITATION.rst).
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