You can use conda
/mamba
with the environment.yml
file in this folder in order to run the example:
We recommend mamba
as it creates environments faster.
# from within the `example` folder:
# 1. create the environment
mamba env create -n feature-selection --file environment.yml
# 2. activate the envrionment
mamba activate feature-selection
# 3. start the jupyter notebook server
# which should open your default browser
jupyter notebook --notebook-dir=.
The main use-case is documented in the feature-selection-example.ipynb
notebook.
There you'll learn how to use the feature selection methods provided by this package to reduce the feature set for a sklearn
Random Forest.
The feature-selection-example-using-vigra.ipynb
notebook shows how to use these methods with a vigra
Random Forest, for those who want to know :).