{"id":19294432,"url":"https://github.com/betaandbit/rml_es","last_synced_at":"2026-03-19T09:08:38.055Z","repository":{"id":70774569,"uuid":"469428157","full_name":"BetaAndBit/RML_ES","owner":"BetaAndBit","description":"The Hitchhiker’s Guide to Responsible Machine Learning - Spanish Edition","archived":false,"fork":false,"pushed_at":"2022-05-03T12:09:01.000Z","size":211286,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-29T03:04:14.082Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://betaandbit.github.io/RML_ES/","language":null,"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/BetaAndBit.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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-13T16:30:14.000Z","updated_at":"2022-03-31T09:14:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"c61bd5f2-0876-4bbc-9880-a2ef88d3815c","html_url":"https://github.com/BetaAndBit/RML_ES","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BetaAndBit/RML_ES","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML_ES","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML_ES/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML_ES/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML_ES/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BetaAndBit","download_url":"https://codeload.github.com/BetaAndBit/RML_ES/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML_ES/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29562420,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-17T21:50:49.831Z","status":"ssl_error","status_checked_at":"2026-02-17T21:46:15.313Z","response_time":100,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":"2024-11-09T22:38:17.866Z","updated_at":"2026-02-17T23:36:29.191Z","avatar_url":"https://github.com/BetaAndBit.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# La Guía del Viajero al Aprendizaje Automático Responsable\n\nPuede comprar la versión en papel en los sitios web:\n\n* [Amazon](https://www.amazon.com/Gu%C3%ADa-Viajero-Aprendizaje-Autom%C3%A1tico-Responsable/dp/8365291142/)\n* [Lulu](https://www.lulu.com/en/en/shop/anna-kozak-and-przemyslaw-biecek-and-aleksander-zawada-and-juan-c-correa/la-gu%C3%ADa-del-viajero-al-aprendizaje-automático-responsable/paperback/product-4zdw4v.html)\n\nLa versión electrónica está disponible de forma gratuita:\n\n* Flipbook: https://betaandbit.github.io/RML_ES/\n* Fragmentos de código reproducibles: [html](https://htmlpreview.github.io/?https://github.com/BetaAndBit/RML/blob/main/data/modelsXAI.html)\n* Fragmentos reproducibles: [Rmd](data/modelsXAI.Rmd)\n* Datos: [covid_spring.csv](data/covid_spring.csv)\n* Datos: [covid_summer.csv](data/covid_summer.csv)\n\n\n## The Hitchhiker’s Guide to Responsible Machine Learning - Spanish Edition\n\nEste libro es un enredo único de teoría, ejemplos y procesos relevantes para el Aprendizaje Automático Responsable. Encontrará intuiciones y ejemplos para el Aprendizaje Automático Interpretable (IML) y la Inteligencia Artificial eXplicable (XAI). Las descripciones se complementan con fragmentos de código con ejemplos para R con el uso de los paquetes `randomForest`, `mlr3` y `DALEX`. Finalmente, el proceso se muestra a través de un cómic que describe las aventuras de dos personajes, Beta y Bit. La interacción de estos dos muestra las decisiones a las que a menudo se enfrentan los analistas, ya sea para probar un modelo diferente, para probar otra técnica de exploración o para buscar otros datos, cuestiones como la comparación de modelos o su validación.\n\nTodos los ejemplos son totalmente reproducibles, de modo que se pueden reproducir todas las aventuras en un escritorio local.\n\nEl desarrollo de modelos es una tarea responsable y desafiante, pero también una aventura apasionante. A veces los libros de texto se centran sólo en la parte técnica, perdiendo toda la diversión. Aquí lo vamos a tener todo.\n\n\nTenga en cuenta que se trata de conjuntos de datos artificiales generados para imitar las relaciones presentes en los datos reales.\n\n\n## Comments\n\n###  Fernando Marmolejo-Ramos\n\nResearch fellow in human and artificial cognition at the Centre for Change and Complexity in Learning at the University of South Australia. \n\nEN:\n\n```\nData science requires knowledge of the data and the methods to parse such data. A new kid\non the analytical block is gaining popularity among data scientists: machine learning. It is\npowerful as it combines long-standing statistical methods with computational versatility. But\nas as the ‘Peter Parker principle’ goes ‘with great power comes great responsibility’. Biecek’s\ntextbook provides a concise tutorial on how to tame the power of machine learning\nresponsibly. This textbook needs to be read by anyone daring to tickle the power of machine\nlearning.\n```\n\nES:\n\n```\nLa ciencia de datos requiere conocimiento de los datos y los métodos para analizar dichos\ndatos. Un nuevo chico en el bloque analítico está ganando popularidad entre los científicos\nde datos: el aprendizaje de máquinas. Es poderoso ya que combina métodos estadísticos\nbien establecidos con versatilidad computacional. Pero como dice el \u0026#39;principio de Peter\nParker\u0026#39;, \u0026#39;un gran poder conlleva una gran responsabilidad\u0026#39;. El libro de texto de Biecek\nproporciona un tutorial conciso sobre cómo dominar el poder del aprendizaje de máquinas\nde manera responsable. Este libro de texto debe ser leído por cualquiera que se atreva a\naprovechar el poder del aprendizaje automático.\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbetaandbit%2Frml_es","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbetaandbit%2Frml_es","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbetaandbit%2Frml_es/lists"}