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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/
Original source
16 responses to “Robust estimation with tidymodels bootstrap resampling”
I want to be as good as you one day
Julia,thanks! Could you make a video about the use of the dials and tune package?
Cheers! Great video!
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
It's very good video, thanks
Thanks, Julia!
I would love to see a vid about your R workflow and shortcuts
your tutorial help me alot 🙂
That ending slays me! You rock.
Nice work, Julia!
Really nice! Tks!!!
Nice! Now if William Gosset had just had YouTube…
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?
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)
)
Nice touch at the end! Sampling some beer to validate your model.
Great video! Thanks for sharing it.