Using fuzzy-rough sets to measure bias in pattern classification problems
This python package contains a bias metric based on the paper "A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets" (Link to publication).
This bias metric measures the extent to which a feature in a dataset influences the outcome variable.
1 maintainer
Metadata
1 maintainer
Release files for fairfru 1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| fairfru-1.tar.gz | 4.1 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| FairFRU-1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.6 kB
Release files / fairfru-1.tar.gz
| Download URL | fairfru-1.tar.gz |
|---|---|
| Size | 4.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2fbfff8820cae9be250ed2c0cb7697b50e992eed3d911950903decb40bd6345b
|
|
BLAKE2b-256 checksum How to use checksums |
48eeb7409d7a1d81cda8476f93a581c312b21ef3587ad3ae50881957831ccb88
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|
Release files / FairFRU-1-py3-none-any.whl
| Download URL | FairFRU-1-py3-none-any.whl |
|---|---|
| Size | 11.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
39402067cb2a0f2675fa4b23d1aa5e564f69b9e4a529acf850a59f774fee6ee7
|
|
BLAKE2b-256 checksum How to use checksums |
873a97937eb3ac17c7f6436315ccaab175512aa5b420a5f1fa3e8d5e47995b1d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|
Security for fairfru 1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
