scikit-weak 0.1.5

A package featuring utilities and algorithms for weakly supervised ML.

# scikit-weak (scikit-weakly-supervised)

A package featuring utilities and algorithms for weakly supervised ML. Should be (more-or-less) compatible with scikit-learn! It collects original algorithms and methods developed by the contributors, as well as some algorithms available in the literature.

Current contributors: - Andrea Campagner, MUDI Lab, University of Milano-Bicocca - Julian Lienen, Paderborn University

## How to install You can install the library using the command:

` pip install scikit-weak `

### Dependencies: numpy, scipy, scikit-learn, tensorflow, keras, pytest

## Documentation The documentation is generated using Sphinx (https://www.sphinx-doc.org/). If you download the source code from this repository you can generate the documentation in html format by typing: ` sphinx-build -b html docs/source docs/build/html ` in the main folder of the project.