Skip to content
Supervised domain-agnostic prediction framework for probabilistic modelling
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs Upgrade deprecated pngmath to imgmath Sep 30, 2018
examples Remove .png export from examples plot Sep 30, 2018
skpro Update to latest scikit and fix dependency regressions Sep 30, 2018
tests
.coveragerc Refactored package structure for publication (alpha) Sep 13, 2017
.gitignore Automatic API reference generation Oct 25, 2017
.travis.yml Upgrade deprecated pngmath to imgmath Sep 30, 2018
AUTHORS.rst Update links to reflect repository transfer Dec 4, 2017
CHANGES.rst Documentation improvements Sep 16, 2017
CITATION.rst Fix unicode in biblatex Jan 3, 2018
CODE_OF_CONDUCT.md Documentation improvements Oct 24, 2017
CONTRIBUTING.md Update links to reflect repository transfer Dec 4, 2017
LICENSE.txt Workflow improvements and license update 3-clause Oct 30, 2017
README.md Adding citation info Jan 3, 2018
github_deploy_key_alan_turing_institute_skpro.enc Configuring doctr keys Dec 8, 2017
requirements.txt Adding PyMC3 to default dependencies Oct 26, 2017
setup.cfg Switch to travis-ci.org Dec 8, 2017
setup.py Switch to travis-ci.org Dec 8, 2017
test-requirements.txt Resolve dependency conflict of pytest-cov Feb 18, 2019

README.md

skpro

PyPI version Build Status License

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.

You can’t perform that action at this time.