Great video. Thanks. Students still present questions like "when do I juice? Before or after baking? Why would I juice something that is to be baked? Can I bake something that isn't prepped? If I use workflows it looks like I can avoid the cooking analogies altogether since they don't seem to naturally extend into workflows", etc. Over time I think jumping straight into the workflows helps show students (and teachers) the framework from a top down approach. We can the use functions at a more basic level to examine what's happening underneath the hood.
Yep, that is how we teach tidymodels in our book, workshops, etc. Take a look at _Tidy Modeling with R_. We introduce models, workflows, and recipes, in Chs 6, 7, and 8, and don't talk about `prep()` or `bake()` at all until Ch 16: www.tmwr.org/dimensionality#recipe-functions We don't teach with `juice()` at all, and just reserve that for expert and internal use.
Thank you for the great video. I have one question, assuming the best model was one of the tuned random forest models, how would we extract the parsnip object to see the tuned hyperparameters i.e mtry and min_n?
You might check out the different "extract" functions in tidymodels. You can do `extract_fit_parsnip()` but you can also do `extract_parameter_set_dials()` to get the hyperparameters directly: hardhat.tidymodels.org/reference/hardhat-extract.html
Thank you very much for such a great screencast. Has the norm changed in using the pipe sign (%>%)? I try to use your style while I code in R. What are the benefits of using |>? Is there a shortcut for it?
There are two posts I think are helpful for understanding the differences between the base R and magrittr pipes: - ivelasq.rbind.io/blog/understanding-the-r-pipe/ - www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/ For most cases, they act the same so no need to stress too much! You can change the shortcut in RStudio to the base R one, if you prefer: stackoverflow.com/questions/68667933/how-to-fast-insert-new-pipe-operator-in-rstudio
Been here from when your introduction had stack overflow in it. You were a tutor once and now just part of my weekend analytical routine. Thank you.
I always learn so much from your tutorial videos! It's exciting to watch the tidymodels framework becoming more and more fleshed out.
Thank you very much for this screencast ,I hope to see your lecteur twice a month ,again ,many thanks .
Many thanks for yet another great lecture!
Great video. Thanks for the clarification on random forests behavior regarding tuning and trees.
Great video. Thanks. Students still present questions like "when do I juice? Before or after baking? Why would I juice something that is to be baked? Can I bake something that isn't prepped? If I use workflows it looks like I can avoid the cooking analogies altogether since they don't seem to naturally extend into workflows", etc. Over time I think jumping straight into the workflows helps show students (and teachers) the framework from a top down approach. We can the use functions at a more basic level to examine what's happening underneath the hood.
Yep, that is how we teach tidymodels in our book, workshops, etc. Take a look at _Tidy Modeling with R_. We introduce models, workflows, and recipes, in Chs 6, 7, and 8, and don't talk about `prep()` or `bake()` at all until Ch 16:
www.tmwr.org/dimensionality#recipe-functions
We don't teach with `juice()` at all, and just reserve that for expert and internal use.
Thank you for the great video. I have one question, assuming the best model was one of the tuned random forest models, how would we extract the parsnip object to see the tuned hyperparameters i.e mtry and min_n?
You might check out the different "extract" functions in tidymodels. You can do `extract_fit_parsnip()` but you can also do `extract_parameter_set_dials()` to get the hyperparameters directly:
hardhat.tidymodels.org/reference/hardhat-extract.html
Can you please go over the time_split method for time-series data ?
Thank you very much for such a great screencast.
Has the norm changed in using the pipe sign (%>%)?
I try to use your style while I code in R. What are the benefits of using |>?
Is there a shortcut for it?
There are two posts I think are helpful for understanding the differences between the base R and magrittr pipes:
- ivelasq.rbind.io/blog/understanding-the-r-pipe/
- www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/
For most cases, they act the same so no need to stress too much!
You can change the shortcut in RStudio to the base R one, if you prefer:
stackoverflow.com/questions/68667933/how-to-fast-insert-new-pipe-operator-in-rstudio