Metadata-Version: 2.4
Name: unicor
Version: 0.2.0
Summary: Hierarchical Feature Selection through Propagation of Uniquely Correlated Entities
Author-email: Sebastian Staab <sebastian.staab@uni-konstanz.de>
License: MIT
Project-URL: Homepage, https://github.com/SebastianStaab/UniCor
Project-URL: Issues, https://github.com/SebastianStaab/UniCor/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

UniCor - Hierarchical Feature Selection through Propagation of Uniquely Correlated Entities

The idea is to utilize the natural hierarchy in high dimensional, hierarchical datasets (like taxonomic hierarchy in microbiome datasets) in order to make them appropriate for a bigger variety of methods through a reduction of their feature space without the loss of relevant information.
The UniCor Metric 
= |fcc| - ffc
identifies UNIquely CORrelated eNtities (UNICORNs) with
- high absolute correlation (feature [cont. target var.] correlation, |fcc|)
- negative or low uniqueness (average feature feature correlation, ffc)
