Dear all, Just thoughts/ideas:
this is interesting discussion as I search a bit for newer books on scikit learn and just how practitioners use it. I believe one thing that can boost visibility/use of such books and scikit learn itself would be like a schematic/workflow how scikit learn is used based on dataset(s) input by user, and task selection (classification, regression, clustering, supervised, unsupervised, semi-supervised ways). Such interactive graphic or tabular way, navigation, would be cool for both beginner practitioners and those expert/professional levels doing applied ML. Just ideas. Kind regards, Dalibor On Thu, Mar 12, 2026, 08:13 Tim Head via scikit-learn < [email protected]> wrote: > Hi Kevin, > > out of interest, how did you decide to use scikit-learn v0.23 in the book? > I was surprised by that because it is a fairly old version. Lots of effort > has gone into scikit-learn since then, so understanding why people choose > (or are forced to?) use such old versions would be useful. > > T > > On Wed, 11 Mar 2026 at 22:38, Kevin Markham <[email protected]> wrote: > >> Hi Thomas, >> >> Thank you so much for your suggestion! I’d love to include that (and many >> of the other newer features) in a future edition of the book, assuming >> there’s enough interest! >> >> Best, >> Kevin >> >> >> On Mar 11, 2026 at 3:33:31 PM, Thomas Fan via scikit-learn < >> [email protected]> wrote: >> >>> Thank you for sharing! >>> >>> In the “Tuning the decision threshold” section, scikit-learn now has a >>> TunedThresholdClassifierCV! >>> It could be worthwhile to include in the second edition. 😆 >>> >>> 3.3. Tuning the decision threshold for class prediction >>> <https://scikit-learn.org/stable/modules/classification_threshold.html#tunedthresholdclassifiercv> >>> scikit-learn.org >>> <https://scikit-learn.org/stable/modules/classification_threshold.html#tunedthresholdclassifiercv> >>> [image: favicon.ico] >>> <https://scikit-learn.org/stable/modules/classification_threshold.html#tunedthresholdclassifiercv> >>> <https://scikit-learn.org/stable/modules/classification_threshold.html#tunedthresholdclassifiercv> >>> >>> Best, >>> Thomas >>> >>> On Mar 11, 2026, at 10:30 AM, Kevin Markham <[email protected]> wrote: >>> >>> >>> 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 >>> >>> _______________________________________________ >>> scikit-learn mailing list -- [email protected] >>> To unsubscribe send an email to [email protected] >>> https://mail.python.org/mailman3//lists/scikit-learn.python.org >>> Member address: [email protected] >>> >>> _______________________________________________ >>> scikit-learn mailing list -- [email protected] >>> To unsubscribe send an email to [email protected] >>> https://mail.python.org/mailman3//lists/scikit-learn.python.org >>> Member address: [email protected] >>> >> _______________________________________________ >> scikit-learn mailing list -- [email protected] >> To unsubscribe send an email to [email protected] >> https://mail.python.org/mailman3//lists/scikit-learn.python.org >> Member address: [email protected] >> > _______________________________________________ > scikit-learn mailing list -- [email protected] > To unsubscribe send an email to [email protected] > https://mail.python.org/mailman3//lists/scikit-learn.python.org > Member address: [email protected] >
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