Model Canadian wind turbine capacity with decision trees and tidymodels

Tune and interpret decision trees for predicting capacity of #TidyTuesday wind turbines in Canada. Check out the code on my blog: juliasilge.com...

[@clono1984]

hi Julia, I'm a huge fan of yours! Just a request for future consideration: an ML workflow with a at least one Python chunk. Would love to learn how you would blend R/Python together.
Thanks for all of your great work.

[@fealgu100]

Thanks for all the great topics, Julia.

[@davidroche2744]

I have learnt a lot with your videos. Thanks Julia.

[@davidjackson7675]

You could have used "span=" in the geom_smooth() to adjust the fit.

[@matthieur.4589]

Awesome, thanks :)

[@maksim0933]

big black cat also listening to the lesson sitting behind ))

[@deanmait]

Hi Julia, Great video as usual. Why did you not use the "workflow" this time? Also when would you typically choose to use that approach instead of the "non-workfow" one and vice versa?

[@JuliaSilge]

I did not use a workflow this time mostly so that I could show how to use parttree for visualization; that only works for bare parsnip models.

[@deanmait]

@@JuliaSilge Got it. Thanks Julia

[@PA_hunter]

Hi Julia, is there a visual of how the different tidymodels steps connect?

[@JuliaSilge]

Two things come to mind for this.
One is this section of our book which has an outline of the modeling process:
www.tmwr.org/software-modeling.html#model-phases
Another is this outline of what the different packages do:
www.tidymodels.org/packages/

[@iugaMovil]

Great video, learned a lot. I'm adding to my toolbox:
- fct_lump_n
- finalize_model

[@syhusada1130]

Is there a way to visualize the trees with its condition at every split and end of tree through tidymodels?

[@JuliaSilge]

If I'm understanding your question correctly, you'll want to use `extract_fit_engine()` and then use any typical visualization such as rpart.plot():
parsnip.tidymodels.org/reference/extract-parsnip.html

[@grvsrm]

Hey Julia, Thanks for another useful screencast. Just a small doubt, while predicting finally using the workflow, I get the following error. I wonder, what could be the reason???
> final_res$.workflow[[1]] %>%
+ predict(turbine_train[44,])
Error: Workflow has not yet been trained. Do you need to call `fit()`?

[@JuliaSilge]

Ah, there is a bug in the current version of tune on CRAN about this. If you can update tune from GitHub, this is fixed. (We are working on a new CRAN release for tune very soon.)

[@grvsrm]

@@JuliaSilge Thanks a lot. Let me do that right away. Thanks again..!

[@artathearta]

5:00 Great video Julia, just a question, why didn't you just use recipes for these steps?

[@JuliaSilge]

You definitely could, especially the `fct_lump_n()` might be something you would want to learn from training data and then apply to testing data. We have to use good judgment in when to use recipes for a transformation vs. when to apply it before starting a modeling workflow (maybe even before splitting into testing and training data). The important things to think about are how information leakage may creep in, whether this is a statistical transformation that you want to learn from one data set and apply to others, whether this is a deterministic transformation that isn't affected by that kind of thing, etc. Some of these here are a bit in a gray area. You can read more about related issues here:
www.tmwr.org/recipes.html#skip-equals-true

[@artathearta]

@@JuliaSilge Thank you for such a thorough response. I've been working through your book (tmwr) with Max Kuhn and I just searched "tidymodels r tutorials" to get my hands a little dirty when I found your videos. Thank you again!

[@mikhaeldito]

I learnt a lot from your videos! How can we tune and select over many models in one pipeline? Is it possible to do so in tidymodels framework?

[@JuliaSilge]

Not over multiple *kinds* of models, as in different algorithms. You still need to set those up as separate tuning runs right now, but then you can pretty fluently compare then during the model evaluation phase, the way you compare different tuning options for the same type of model.

[@chubby1985]

Which RStudio Theme is that?

[@JuliaSilge]

It is one of the ones from the rsthemes package, I think? github.com/gadenbuie/rsthemes

[@davidjackson7675]

Julia, Here is my Part-1 of my wind turbine analysis: czcams.com/video/qiOyCi-MH88/video.html