{"id":13704466,"url":"https://github.com/ModelOriented/EloML","last_synced_at":"2025-05-05T10:30:26.265Z","repository":{"id":98024435,"uuid":"206395947","full_name":"ModelOriented/EloML","owner":"ModelOriented","description":"R package EloML: Elo rating system for machine learning models","archived":false,"fork":false,"pushed_at":"2022-06-17T16:24:39.000Z","size":796,"stargazers_count":24,"open_issues_count":3,"forks_count":3,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-08-03T21:05:10.230Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://modeloriented.github.io/EloML/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ModelOriented.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-09-04T19:21:29.000Z","updated_at":"2024-04-22T16:56:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"766b62cc-9f3f-466d-9e00-8bfe26ef73b7","html_url":"https://github.com/ModelOriented/EloML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ModelOriented%2FEloML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ModelOriented%2FEloML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ModelOriented%2FEloML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ModelOriented%2FEloML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ModelOriented","download_url":"https://codeload.github.com/ModelOriented/EloML/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224439981,"owners_count":17311563,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T21:01:10.479Z","updated_at":"2024-11-13T11:31:25.954Z","avatar_url":"https://github.com/ModelOriented.png","language":"R","funding_links":[],"categories":["Tools"],"sub_categories":["Performance (\u0026 Automated ML)"],"readme":"# Elo rating system for Machine Learning models \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"150\"/\u003e\n\n[![CRAN\\_Status\\_Badge](http://www.r-pkg.org/badges/version/EloML)](https://cran.r-project.org/package=EloML)\n\u003cimg src=\"http://cranlogs.r-pkg.org/badges/grand-total/EloML\" /\u003e\n[![Build\nStatus](https://travis-ci.org/ModelOriented/EloML.svg?branch=master)](https://travis-ci.org/ModelOriented/EloML)\n[![Coverage\nStatus](https://img.shields.io/codecov/c/github/modeloriented/EloML/master.svg)](https://codecov.io/github/modeloriented/EloML?branch=master)\n\n## Overview\n\nThe `EloML` package provides Elo rating system for machine learning models. Elo Predictive Power (EPP) score helps to assess model performance based Elo ranking system. \n\nFind more in the [EPP: interpretable score of model predictive power](https://arxiv.org/abs/1908.09213) arxiv paper.\n\n\n## Installation\n\nInstallation time should not exceed 1 minute.\n\n```r\n# Install the the development version from GitHub:\n# install.packages(\"devtools\")\ndevtools::install_github(\"ModelOriented/EloML\")\n```\n\n## Usage\n\nThe following example takes less than 20 seconds to complete.\n\nLoad `EloML` library and benchmark data. In the example we use the data frame `auc_data` from the `EloML` package. The data used for EPP calculations should be a data frame, where first 3 columns correspond to: Player (`model`), Round (`split`), Score (`auc`).\n\n```{r}\nlibrary(EloML)\ndata(auc_scores)\n\nhead(auc_scores)\n\n#        model split       auc\n# 1 catboost_1     1 0.9824724\n# 2 catboost_1     2 0.9820267\n# 3 catboost_1     3 0.9801000\n# 4 catboost_1     4 0.9848932\n# 5 catboost_1     5 0.9845456\n# 6 catboost_1     6 0.9858062\n\n```\n\nTo calculate EPP use `calculate_epp` function. For more options see help of the function `?calculate_epp`.\n\n```{r}\ncalculate_epp(auc_scores)\n\n# Head of Players EPP: \n#       player        epp\n# 1 catboost_1  -0.793627\n# 2 catboost_2   2.915507\n# 3 catboost_3  -1.990134\n# 4      gbm_1 -20.381584\n# 5     gbm_10   1.664303\n# 6     gbm_11   2.714073\n# Type of estimation:  glmnet\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FModelOriented%2FEloML","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FModelOriented%2FEloML","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FModelOriented%2FEloML/lists"}