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Supervised learning for probabilistic prediction

Project description

![skpro](/docs/_static/logo/logo.png)

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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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