• Wednesday , 12 August 2020

Robust estimation with tidymodels bootstrap resampling

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See how to use bootstrap resampling to estimate model parameters using #TidyTuesday data on beer production, with code available here: https://juliasilge.com/blog/beer-production/

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16 Comments

  1. World's Greatest Swordsman
    July 7, 2020 at 20:07

    I want to be as good as you one day

  2. gtalckmin
    July 7, 2020 at 20:07

    Julia,thanks! Could you make a video about the use of the dials and tune package?

  3. Hani Shamseddeen
    July 7, 2020 at 20:07

    Cheers! Great video!

  4. Christopher Yee
    July 7, 2020 at 20:07

    thank you, julia!

    question: what if I just want to unnest all splits instead of building a model? i tried unnest(map(splits, as.data.frame)) with no success

  5. Minh Nguyen Bui
    July 7, 2020 at 20:07

    It's very good video, thanks

  6. Pete Talbert
    July 7, 2020 at 20:07

    Thanks, Julia!

  7. Norhther
    July 7, 2020 at 20:07

    I would love to see a vid about your R workflow and shortcuts

  8. Ashish kumar
    July 7, 2020 at 20:07

    your tutorial help me alot 🙂

  9. Erick Knackstedt
    July 7, 2020 at 20:07

    That ending slays me! You rock.

  10. Mubashir Qasim
    July 7, 2020 at 20:07

    Nice work, Julia!

  11. Ale Lust
    July 7, 2020 at 20:07

    Really nice! Tks!!!

  12. Chuck Burks
    July 7, 2020 at 20:07

    Nice! Now if William Gosset had just had YouTube…

  13. Louis Maiden
    July 7, 2020 at 20:07

    This is fantastic! Can you explain which specific violations of the assumptions of linear models would lead us to use bootstrap resampling for better estimates?

  14. Viviane Sanchez
    July 7, 2020 at 20:07

    Hi Julia! Great post! I was wondering, if I did the same process, but with decision trees instead of a linear model, would that be bagging? If so, is there a way to introduce the tidy model recipe process in the model column below or should I use the the package for fitting directly? (rpart for example). Thanks!

    beer_models <- beer_boot %>%
    mutate(
    model = map(splits, ~ lm(sugar_and_syrups ~ 0 + malt_and_malt_products, data = .)),
    coef_info = map(model, tidy)
    )

  15. Jas Sohi
    July 7, 2020 at 20:07

    Nice touch at the end! Sampling some beer to validate your model.

  16. SquashBox
    July 7, 2020 at 20:07

    Great video! Thanks for sharing it.

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