Mailman 3 March 2026 - scikit-learn

Re: scikit-learn Digest, Vol 116, Issue 6
by stefanie.senger＠posteo.de March 26, 2026

March 26, 2026

Hello Anoop J S, I can see the NumFocus project idea list for GSoC 2026 here: https://github.com/numfocus/gsoc/blob/master/2026/ideas-list.md Scikit-learn is not on the list this year. :( Best! Am 26.03.2026 10:37 schrieb scikit-learn-request(a)python.org: > Send scikit-learn mailing list submissions to > scikit-learn(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman3/lists/scikit-learn.python.org/ > or, via email, send a message with subject or body 'help' to > scikit-learn-request(a)python.org > > You can reach the person managing the list at > scikit-learn-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > Today's Topics: > > 1. GSoC 2026 Application - First PR Submitted - Seeking Guidance > (Anoop) > 2. Re: GSoC 2026 Application - First PR Submitted - Seeking Guidance > (Adrin) > 3. Fwd: GSoC 2026 Application - First PR Submitted - Seeking > Guidance > (Adrin) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Thu, 26 Mar 2026 02:24:10 +0530 > From: Anoop <anoopjsbethanya(a)gmail.com> > Subject: [scikit-learn] GSoC 2026 Application - First PR Submitted - > Seeking Guidance > To: scikit-learn(a)python.org > Message-ID: > <CAGW9n3u1G4pQs=NZmzi7QyfqUCdT_xPN0YD55sx0Fw5pqEduQw(a)mail.gmail.com> > Content-Type: multipart/alternative; > boundary="000000000000ce5f72064ddf78a8" > > Hi scikit-learn community! > I'm Anoop J S from India, a Python developer interested in AI/ML. I am > applying for GSoC 2026 under NumFOCUS/scikit-learn. > I just submitted my first PR today: > https://github.com/Anoop-j-s/scikit-learn/pull/1 > It fixes Issue #31164 - ConvergenceWarning and typo in > plot_gpr_on_structured_data.py > I would love guidance on: > - GSoC 2026 project ideas for beginners > - Tips for writing a strong proposal > > Thank you! > Anoop J S > GitHub: https://github.com/Anoop-j-s > Email: anoopjsbethanya(a)gmail.com >

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GSoC 2026 Application - First PR Submitted - Seeking Guidance
by Anoop March 26, 2026

March 26, 2026

Hi scikit-learn community! I'm Anoop J S from India, a Python developer interested in AI/ML. I am applying for GSoC 2026 under NumFOCUS/scikit-learn. I just submitted my first PR today: https://github.com/Anoop-j-s/scikit-learn/pull/1 It fixes Issue #31164 - ConvergenceWarning and typo in plot_gpr_on_structured_data.py I would love guidance on: - GSoC 2026 project ideas for beginners - Tips for writing a strong proposal Thank you! Anoop J S GitHub: https://github.com/Anoop-j-s Email: anoopjsbethanya(a)gmail.com

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Final CFP MultiClinAI Shared Task (SMM4H-HeaRD in the ACL 2026)
by Martin Krallinger March 16, 2026

March 16, 2026

Final CFP: Multilingual Automatic Clinical Corpus Generation and Entity Extraction https://temu.bsc.es/MultiClinAI/ *Updates: Evaluation evaluation library released MultiClinAI is the first shared task focused on (1) the automatic creation of comparable multilingual corpora and (2) the automatic detection of key clinical concepts (diseases, symptoms, and procedures) in seven languages: Spanish, English, Italian, Dutch, Romanian, Swedish and Czech. MultiClinAI will be held as part of the #SMM4H-HeaRD Workshop at the ACL 2026 conference (online). Key information: - Web: https://temu.bsc.es/MultiClinAI/ <https://mailtrack.io/trace/link/b82969cdff5285721c515b5fe3b2af2c28abd34e?ur…> - Data: <https://mailtrack.io/trace/link/5ccb81bce7b07971c4c79b11f735aa6eee7cb0f8?ur…> https://zenodo.org/records/18772832 - Annotation guidelines: <https://mailtrack.io/trace/link/92a08d209c6530f84b412c380ae19e8760b8d31b?ur…> https://zenodo.org/records/13151040 - Registration: <https://mailtrack.io/trace/link/0dfb76782b3e7e5cd3ca2e60e694cabccb150191?ur…> https://temu.bsc.es/MultiClinAI/registration/ - *Evaluation Library: <https://mailtrack.io/trace/link/0dfb76782b3e7e5cd3ca2e60e694cabccb150191?ur…> https://github.com/nlp4bia-bsc/MultiClinAIEval Motivation Despite recent progress in clinical language technology solutions there are few high-quality annotated corpora, datasets, and annotation guidelines available for the training/evaluation of NLP- or LLM-based clinical entity recognition systems beyond English. There is a need to foster the generation of annotated datasets in multiple languages ensuring also that they align in terms of annotation criteria to generate comparable labeled datasets across languages and promote comparable entity extraction systems. Developing multilingual models helps reduce linguistic bias and improves the global applicability of clinical language technologies. Such models enable more equitable AI deployment across different regions and healthcare systems. Multilingual clinical NLP has numerous important use cases including: - In international clinical trials, it can be used to extract structured data from trial sites across different countries and to ensure consistent outcome definitions across languages. - For cohort identification, it enables the identification of eligible patients from unstructured electronic health records (EHRs) and the extraction of phenotypes for observational studies. -In disease surveillance, multilingual systems can help detect rare diseases or emerging health trends and identify post-marketing drug safety signals. In this context, the MultiClinAI (Multilingual Clinical Entity Annotation Projection and Extraction) shared task addresses the creation and evaluation of comparable multilingual clinical resources across seven languages, focusing on three key entity types: diseases, symptoms, and procedures. - MultiClinNER subtask: multilingual clinical named entity recognition across expert-annotated gold-standard datasets. - MultiClinCorpus subtask: automatic generation of comparable multilingual clinical corpora through annotation projection techniques. This setup will enable a robust benchmarking scenario for multilingual clinical NLP approaches. Schedule - MultiClinAI Shared Task – training set release (February 6, 2026) - MultiClinNER test set release (March 18, 2026) - MultiClinNER test set prediction submissions (March 25, 2026) - MultiClinCorpus test set release (March 27, 2026) - MultiClinCorpus test set prediction submissions (April 9, 2026) - Result / evaluation returned to teams (April 14, 2026) - Participant proceedings due (April 24, 2026) - Notification of acceptance (May 15, 2026) - Camera-ready papers due (May 25, 2026) - ACL Proceedings due (hard deadline) (June 1, 2026) - Workshop (online) (July 2–3, 2026) Publications and SMM4H-HeaRD in the ACL 2026 workshop Teams participating in MultiClinAI will be invited to contribute a systems description paper for the ACL 2026 Working Notes proceedings and a short online presentation of their approach at the ACL 2026 workshop (online). Main Organizers - Salvador Lima-López, Barcelona Supercomputing Center (BSC), Spain. - Fernando Gallego-Donoso, Barcelona Supercomputing Center (BSC), Spain. - Jan Rodríguez-Miret, Barcelona Supercomputing Center (BSC), Spain. - Judith Rosell, Barcelona Supercomputing Center (BSC), Spain. - Martin Krallinger, Barcelona Supercomputing Center (BSC), Spain. Scientific Committee - Francisco M. Couto, Universidade de Lisboa, Portugal. - Ulf Leser, Humboldt-Universität zu Berlin, Germany. - Guergana Savova, Boston Children’s Hospital, United States. - Lourdes Araujo, Universidad Nacional de Educación a Distancia, Spain. - Pavel Pecina, Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Czech Republic. - Halil Kilicoglu, University of Illinois at Urbana-Champaign, United States. - Rodrigo Agerri, HiTZ Centre of the University of the Basque Country, Spain.

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Master Machine Learning with scikit-learn (free book)
by Kevin Markham March 12, 2026

March 12, 2026

Hello scikit-learn community! Last week I published a new book called “Master Machine Learning with scikit-learn”, and as my gift to the community, I’ve made it free to read online (with no ads and no registration required): https://mlbook.dataschool.io I designed it to be a “practitioner’s handbook" for the effective use of scikit-learn, covering many practical topics that I’ve not seen covered elsewhere. I hope that it fills a useful role in the ecosystem of scikit-learn educational resources, and perhaps even plays a tiny role in ensuring the long-term success of the library. (I’ve already heard from two teachers who would like to use the book in their classrooms, which is great news!) If you're a current or former Core Contributor, I would be honored to send you a free paperback copy as a token of my appreciation. Just reply to me (not the list!) with your mailing address and I’ll ship you a copy from Amazon. (It’s already available in a dozen countries, and I’m actively working to expand distribution elsewhere.) Thanks! Kevin P.S. In case you’re curious, I used Quarto to output the book website, ebook (PDF and EPUB), and paperback from a single set of source files. Quarto is amazing!!! Kevin Markham Founder, Data School ML book on Amazon: https://geni.us/MasterML Data Science courses: https://courses.dataschool.io

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