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Supervised domain-agnostic prediction framework for probabilistic modelling


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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

Documentation

The full documentation is available here.

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.

If you use skpro in a scientific publication, we would appreciate citations.

GitHub

https://github.com/alan-turing-institute/skpro


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