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
===========================================================================================================================
Latest Changes
/\
|| 1.0.8 -- bug fixes
|| 1.0.7 -- path fixes v4
|| 1.0.6 -- path fixes v3
|| 1.0.5 -- path fixes v2
|| 1.0.4 -- path fixes
|| 1.0.3 -- path fixes for users to use the function call
|| 1.0.2 -- bug fixes for users to use the function call
|| 1.0.0.2 -- Bug fixes, path fixes.
|| 1.0.0.1 -- Bug fixes, path fixes.
|| 1.0.0 -- First Release to PYPI
/\
Oldest changes
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file PBFS-1.0.8.tar.gz.
File metadata
- Download URL: PBFS-1.0.8.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
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3ee270a09e9a1aaedb8f5964df22362bddfd19a95d69a69128def5f46011924b
|
|
| MD5 |
b7f33b47f62504a0d67a087e900f08f8
|
|
| BLAKE2b-256 |
c388f34b4ee0c421ebe6f72faf874d7e1aef03cc8b0898458ba8af8747bda215
|
