{"id":19294452,"url":"https://github.com/betaandbit/rml","last_synced_at":"2026-03-01T12:34:01.481Z","repository":{"id":55051856,"uuid":"398553258","full_name":"BetaAndBit/RML","owner":"BetaAndBit","description":"The Hitchhiker’s Guide to Responsible Machine Learning","archived":false,"fork":false,"pushed_at":"2023-04-16T16:39:31.000Z","size":263133,"stargazers_count":60,"open_issues_count":0,"forks_count":7,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-02-24T00:28:06.568Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://betaandbit.github.io/RML/","language":"HTML","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":"2021-08-21T12:33:41.000Z","updated_at":"2024-05-26T14:14:32.000Z","dependencies_parsed_at":"2024-11-09T22:48:33.086Z","dependency_job_id":null,"html_url":"https://github.com/BetaAndBit/RML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BetaAndBit/RML","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BetaAndBit","download_url":"https://codeload.github.com/BetaAndBit/RML/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BetaAndBit%2FRML/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29969245,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-01T11:43:06.159Z","status":"ssl_error","status_checked_at":"2026-03-01T11:43:03.887Z","response_time":124,"last_error":"SSL_read: 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":"2024-11-09T22:38:28.057Z","updated_at":"2026-03-01T12:33:56.459Z","avatar_url":"https://github.com/BetaAndBit.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# The Hitchhiker’s Guide to Responsible Machine Learning\n\nThis book is a unique entanglement of theory, examples and processes relevant to Responsible Machine Learning. You will find intuitions and examples for Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI). Descriptions are supplemented by code snippets with examples for R with the use of randomForest, mlr3 and DALEX packages. Finally, the process is shown through a comic book describing the adventures of two characters, Beta and Bit. The interaction of these two shows the decisions that analysts often face, whether to try a different model, try another technique for exploration or look for other data -- questions like how to compare models or validate them.\n\nAll examples are fully reproducible so that one can replay all adventures on a local desktop.\n\nModel development is a responsible and challenging task but also an exciting adventure. Sometimes textbooks focus only on the technical side, losing all the fun. Here we are going to have it all.\n\n## Materials \n\n* Free flipbook: https://betaandbit.github.io/RML/\n* Buy pdf version at LeanPub: https://leanpub.com/RML\n* Buy printed paper version at LuLu: https://www.lulu.com/shop/anna-kozak-and-przemys%C5%82aw-biecek-and-aleksander-zawada/the-hitchhikers-guide-to-responsible-machine-learning/paperback/product-7nmmmk.html\n* Reproducible code snippets: [html](https://htmlpreview.github.io/?https://github.com/BetaAndBit/RML/blob/main/data/modelsXAI.html)\n* Reproducible snippets: [Rmd](data/modelsXAI.Rmd)\n* Data: [covid_spring.csv](data/covid_spring.csv)\n* Data: [covid_summer.csv](data/covid_summer.csv)\n\nPlease, note that these are artificial datasets generated to mimic relations present in the real data.\n\n## Other language versions\n\n* Polish: *Wprowadzenie do Modelowania Predykcyjnego* [GitHub](https://github.com/BetaAndBit/MiniML), [flipbook](https://betaandbit.github.io/MiniML/)\n* Spanish: *La Guía del Viajero al Aprendizaje Automático Responsable* [GitHub](https://github.com/BetaAndBit/RML_ES), [flipbook](https://betaandbit.github.io/RML_ES/)\n* German: *Per Anhalter durch die Galaxis des verantwortungsvollen maschinellen Lernens* [flipbook](https://betaandbit.github.io/RML_DE/), [paperback](https://www.amazon.de/-/en/Przemyslaw-Biecek/dp/8365291185/)\n* Turkish: *Sorumlu Makine Öğrenmesi Rehberi* [GitHub](https://github.com/BetaAndBit/RML_TR), [flipbook](https://betaandbit.github.io/RML_TR/)\n* Vietnamese: *4.0.1 Cùng xây dựng Model Machine Learning với Bêta và Bít* [GitHub](https://github.com/BetaAndBit/RML_VN), [flipbook](https://betaandbit.github.io/RML_VN/)\n\n## Intro\n\n![intro.png](intro.png)\n\n\n## Comments\n\n### Dianne Cook\n\n\nEN: Dianne Cook is Professor of Business Analytics at Monash University in Melbourne, Australia. Her research is in the area of data visualisation, especially the visualisation of high-dimensional data using tours with low-dimensional projections, and projection pursuit. A current focus is on bridging the gap between exploratory graphics and statistical inference.\n\n```\nThe Hitchhikers Guide to Responsible Machine Learning is a delightful read. I had to flip \nfrom comic to comic first, which is fun and also on point about not falling into a pit \nof errors when doing machine learning with data. The detailed text explanations and \nbeautifully constructed margin figures provide filling to the sandwich. \nCongratulations to Przemek, Anna and Aleksander for a creative and insightful \ncontribution to the explainable AI literature.\n\nDi Cook, Hitchhiker in high-dimensional spaces\n```\n\n### Marek Stączek\n\nCoach, storyteller, author of books\n\nPL:\n\n```\nDwóch studentów Politechniki Warszawskiej szło łeb w łeb w konkursie ogłoszonym przez NASA. Wygrał Mietek Bekker \ni to jego Lunar Roving Vehicle, w statku Apollo poleciał na księżyc.  Jak rodzą się tacy studenci? Przez kontakt \nz wyjątkowymi nauczycielami. A co charakteryzuje tych drugich? Oryginalny i inspirujący przekaz.\n\nDo Waszych rąk trafia podręcznik profesora Przemysława Biecka, który nawiązuje do takich wzorców.\n```\n\nEN:\n\n```\nTwo students from the Warsaw University of Technology went head to head in a competition announced by NASA. \nMietek Bekker won, and it was his Lunar Roving Vehicle in the Apollo spacecraft that flew to the moon.  \nHow are such students born? Through contact with exceptional tutors. And what characterises the latter? \nAn original and inspiring delivery.\n\nAn exceptional textbook by professor Przemysław Biecek is now in your hands.\n```\n\n###  Łukasz Rajkowski\n\neditorial board of Polish popular science monthly Delta\n\n\nPL:\n\n```\nNiniejsza książka jest krótką, lecz pouczającą i wciągającą wycieczką po odpowiedzialnym uczeniu maszynowym, \nw której dokładne wytłumaczenia fundamentalnych koncepcji są umiejętnie wplecione we wdzięczną i atrakcyjnie \nzilustrowaną fabułę. W sposób przyjazny turyście wskazuje ona na pewne ważne aspekty wnioskowania z danych \noraz pozwala rzucić okiem na to, jak tego typu analizy są (lub przynajmniej powinny być) przeprowadzane. \nNależy zwrócić uwagę, że od Czytelnika nie wymaga się profesjonalnego, podróżniczego ekwipunku - otwarty \numysł oraz solidne matematyczne przygotowanie na poziomie szkoły średniej z pewnością wystarczą. Nie trzeba \nchyba dodawać, że nie wystarczy powrócić z tej wycieczki aby stać się ekspertem w \"data science\" - jest to \nwszak ogromna (i fascynująca) dziedzina, która może być porównana do podróży tysiąca mil. Wiemy jednak \ndoskonale, że takie podróże rozpoczynają się od pierwszego kroku... lub złapania autostopu!\n```\n\nEN:\n\n```\nThis book is a short but illuminating and entertaining trip to responsible machine learning, in which \naccurate explanations of some fundamental concepts successfully mingle with a pleasant, richly illustrated \nstoryline. In a tourist-friendly manner it points out some important aspects of inference from the data \nand gives you a glimpse of how data driven answers are (or at least should be) obtained. Note that no \nprofessional travelling equipment is required - an open mind and a solid high-school level of mathematical \nabilities will certainly suffice. Needless to say, finishing this tour won't make you an expert in data \nscience - a vast and fascinating field which can be compared to a journey of a thousand miles. But we all \nknow that such journeys begin with a single step... or a hitchhiker's guide!\n```\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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbetaandbit%2Frml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbetaandbit%2Frml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbetaandbit%2Frml/lists"}