Тёмный
No video :(

Robust estimation with tidymodels bootstrap resampling 

Julia Silge
Подписаться 15 тыс.
Просмотров 6 тыс.
50% 1

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

Опубликовано:

 

28 авг 2024

Поделиться:

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист
Посмотреть позже
Комментарии : 32   
@jsohi
@jsohi 4 года назад
Nice touch at the end! Sampling some beer to validate your model.
@scharlesworth93
@scharlesworth93 4 года назад
SPOILERS
@pipertripp
@pipertripp Год назад
Perfect excuse to crack a beer, kick back, and enjoy!
@Norhther
@Norhther 4 года назад
I would love to see a vid about your R workflow and shortcuts
@guanyuliu4876
@guanyuliu4876 Год назад
Your videos are AMAZING!!!❤❤❤
@erickknackstedt3131
@erickknackstedt3131 4 года назад
That ending slays me! You rock.
@mubiqas
@mubiqas 4 года назад
Nice work, Julia!
@petetalbert
@petetalbert 4 года назад
Thanks, Julia!
@SquashBox
@SquashBox 4 года назад
Great video! Thanks for sharing it.
@narak2273
@narak2273 3 года назад
Nice presentation. Can I request if you could please show Machine learning approaches for predictive modelling using clinical covariates to predict diseases such as diabetes and cardiac arrest. Thanks
@minhnguyenbui6827
@minhnguyenbui6827 4 года назад
It's very good video, thanks
@gtalckmin
@gtalckmin 4 года назад
Julia,thanks! Could you make a video about the use of the dials and tune package?
@JuliaSilge
@JuliaSilge 4 года назад
This video covers tuning and creating parameter values to try using dials: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-hpudxAmxHSM.html
@ashishasashu
@ashishasashu 4 года назад
your tutorial help me alot :)
@Likwidl
@Likwidl 4 года назад
Cheers! Great video!
@arash4232
@arash4232 10 месяцев назад
Hi, do you have videos about wavelet analysis as well?
@alelust7170
@alelust7170 4 года назад
Really nice! Tks!!!
@louismaiden8360
@louismaiden8360 4 года назад
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?
@JuliaSilge
@JuliaSilge 4 года назад
I think this part of Modern Dive has a nice explanation: moderndive.com/8-confidence-intervals.html#ci-build-up
@louismaiden8360
@louismaiden8360 4 года назад
@@JuliaSilge Thanks so much!
@rashawnhoward564
@rashawnhoward564 4 года назад
I want to be as good as you one day
@Rdominca
@Rdominca 4 года назад
Nice! Now if William Gosset had just had RU-vid...
@eeysirhc
@eeysirhc 4 года назад
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
@JuliaSilge
@JuliaSilge 4 года назад
I'm not entirely sure what you are wanting to do, I'm afraid. If you want to open an issue on GH with more specifics, feel free!
@vivi1311
@vivi1311 4 года назад
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 % mutate( model = map(splits, ~ lm(sugar_and_syrups ~ 0 + malt_and_malt_products, data = .)), coef_info = map(model, tidy) )
@JuliaSilge
@JuliaSilge 4 года назад
If you combine them into an aggregated prediction, yes! I like this explanation: bradleyboehmke.github.io/HOML/bagging.html The rsample package FYI was created to be super flexible for many purposes and thus isn't the most performant (if you actually wanted to use this for bagging). You might want to look into the strapgod package: github.com/DavisVaughan/strapgod
@vivi1311
@vivi1311 4 года назад
@@JuliaSilge thank you! =]
@apy9484
@apy9484 3 года назад
Hi Julia, thank you for your video. I am trying to apply this to my dataset and an error shows up. The bootstrapping bit worked just fine. In the next step, when applying my model (a function called anova_test from the package rstatix) to the bootstrapping: dataA.boot %>% mutate(model=map(splits,~anova_test( data=.,dv=fitA,wid=subject,between=c(exp), within=c(Oddball,PvN))), coef_info=map(model,tidy)) I get the error: Error: Problem with `mutate()` input `model`. x Can't subset columns that don't exist. x Column `fitA` doesn't exist. ℹ Input `model` is `map(...)`. I'd be really greatful if you could point me into how to fix this error. Thank you very much!
@JuliaSilge
@JuliaSilge 3 года назад
It's hard to debug this much code in comments like this. I suggest you create a reprex and post on RStudio Community, where it is easier to go back and forth to solve a problem like this one: rstd.io/tidymodels-community
@apy9484
@apy9484 3 года назад
@@JuliaSilge Thanks Julia! I posted it on R studio and you commented there as well, I now have the reprex, however, I am unable to upload my response on R studio (not too sure why). Would it be possible to send it some other way? Thank you very much & sorry for the inconvenience! This is the link to my question: community.rstudio.com/t/problem-using-bootstraps-from-tidyverse-with-anova-test-from-rstatix/87463/3
@JuliaSilge
@JuliaSilge 3 года назад
@@apy9484 If you are having trouble using reprex, it's definitely worth it in the long run to figure out how to use it. Check out this article that digs into some tips: reprex.tidyverse.org/articles/articles/magic-reprex.html
@apy9484
@apy9484 3 года назад
@@JuliaSilge Hi Julia, my issue was not creating the reprex but being able to respond to your comment on R studio. I have just created a new topic, including the reprex. Sorry for the inconvenience. community.rstudio.com/t/problem-using-bootstraps-from-tidyverse-with-anova-test-from-rstatix-2/88125 Thank you very much for your help!!
Далее
Tuning XGBoost using tidymodels
50:36
Просмотров 18 тыс.
나랑 아빠가 아이스크림 먹을 때
00:15
Просмотров 4 млн
Lasso regression with tidymodels and The Office
44:49
3 Reasons to Use Tidymodels with Julia Silge
1:23:53
Просмотров 3,8 тыс.