Exceptional Model Mining (EMM)

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for zbenmo from gravatar.com zbenmo

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR U...)
  • Author: Oren Zeev-Ben-Mordehai
  • Maintainer: zbenmo@gmail.com
Classifiers
Report project as malware

Project description

emmerald

Exceptional Model Mining (EMM)

Based on algorithm and method developed and published here:

Wouter Duivesteijn, Ad J. Feelders, and Arno Knobbe.
Exceptional Model Mining.
Data Mining and Knowledge Discovery 30(1):47–98, 2016.

pip install emmerald

And check the titanic and adult examples.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for zbenmo from gravatar.com zbenmo

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR U...)
  • Author: Oren Zeev-Ben-Mordehai
  • Maintainer: zbenmo@gmail.com
Classifiers

Release history Release notifications | RSS feed

This version

0.1.2

Download files

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

Source Distribution

emmerald-0.1.2.tar.gz (13.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

emmerald-0.1.2-py3-none-any.whl (13.8 kB view details)

Uploaded Python 3

File details

Details for the file emmerald-0.1.2.tar.gz.

File metadata

  • Download URL: emmerald-0.1.2.tar.gz
  • Upload date:
  • Size: 13.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.10

File hashes

Hashes for emmerald-0.1.2.tar.gz
Algorithm Hash digest
SHA256 2acf0852ad23dfc1b7e85a62a7b2111da15bdfb5896d23eed9784f4c5454d940
MD5 724c011dec3c88bb1fd5dbe0eb77b1ac
BLAKE2b-256 c482e903a2598db99288f5887136c75dead522bc8440fa008123a6526ec53b3c

See more details on using hashes here.

File details

Details for the file emmerald-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: emmerald-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 13.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.10

File hashes

Hashes for emmerald-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 654f72cf4c66777811dd217f1d6bd9b443110cabe6608336a7ceaf38d29b8134
MD5 37ebd7656030cfe30a5430b6e8923f9b
BLAKE2b-256 496583e7194825466093e2844fefd1bd1ac776443c0d8b9396493a06f7fbf389

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