Skip to main content

Performance Based Feature selection Technique: Prototype

Project description

This is a prototype Feature Selection library.

The library has functions which predict the effect of features on ML models based on pre-trained ML models. Library owners: Movin Fernandes, Hong ZHU.

This was created as a part of Dissertation project for MSc Data Analytics alongside Research.

CHANGE LOG For Performance based Feature Selection Technique

===========================================================================================================================

1.0.0 -- First Release to PYPI

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PBFS-1.0.0.tar.gz (13.5 MB view details)

Uploaded Source

File details

Details for the file PBFS-1.0.0.tar.gz.

File metadata

  • Download URL: PBFS-1.0.0.tar.gz
  • Upload date:
  • Size: 13.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.10

File hashes

Hashes for PBFS-1.0.0.tar.gz
Algorithm Hash digest
SHA256 e136a0c9852019cbf5b06a9cd2089de63e6fb7ce6bb2595a710a51a124ebe2de
MD5 7b80ad2a19975df7d1a0218f8b011c13
BLAKE2b-256 0d8dc398bda954f31f98d0578ed04dc009462936a2c4c6e0c1a9db8156f45700

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page