Skip to main content

CompStats implements an evaluation methodology for statistically analyzing competition results and competition

Project description

https://github.com/INGEOTEC/CompStats/actions/workflows/test.yaml/badge.svg https://coveralls.io/repos/github/INGEOTEC/CompStats/badge.svg?branch=develop https://badge.fury.io/py/CompStats.svg https://dev.azure.com/conda-forge/feedstock-builds/_apis/build/status/compstats-feedstock?branchName=main https://img.shields.io/conda/vn/conda-forge/compstats.svg https://img.shields.io/conda/pn/conda-forge/compstats.svg https://readthedocs.org/projects/compstats/badge/?version=latest https://colab.research.google.com/assets/colab-badge.svg

Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms’ performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems’ performance. CompStats implements an evaluation methodology for statistically analyzing competition results and competition. CompStats offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals.

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

compstats-0.1.0.tar.gz (22.7 kB view details)

Uploaded Source

Built Distribution

CompStats-0.1.0-py3-none-any.whl (27.9 kB view details)

Uploaded Python 3

File details

Details for the file compstats-0.1.0.tar.gz.

File metadata

  • Download URL: compstats-0.1.0.tar.gz
  • Upload date:
  • Size: 22.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.14

File hashes

Hashes for compstats-0.1.0.tar.gz
Algorithm Hash digest
SHA256 8b0956e66490d2882284db61f2ba02ad2a51a76f011e2e33deda600104913490
MD5 d71265ff452b1d209e99072719620b0c
BLAKE2b-256 888a5181ab5899996e8e1a25dcaf6ca5d84ef5c4627134c3a34c3aebfeb35b70

See more details on using hashes here.

File details

Details for the file CompStats-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: CompStats-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 27.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.19

File hashes

Hashes for CompStats-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a2b0e24d72360d6e3b6d645601aad2a74a4ee3ac440f074eecab66093407e280
MD5 9fc07c96d380f2b190bbb9cb7bd0cf24
BLAKE2b-256 da75cc56c9e683f8d1eef9d8470d64fcaa789e834d950be85de08a8be93f289a

See more details on using hashes here.

Supported by

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