Explore art media over time in the #TidyTuesday Tate collection dataset | Julia Silge

[utterances-bot]

Explore art media over time in the #TidyTuesday Tate collection dataset | Julia Silge

Check residuals and other model diagnostics for regression models trained on text features, all with tidymodels functions.

https://juliasilge.com/blog/tate-collection/

[mwilson19]

pull_workflow_fit(art_final$.workflow[[1]])

That's a strange syntax, funny I was having issues with glmnet in tidymodels as I was misunderstanding how to pull the right iteration with the specified penalty, interesting to see how you do this. I think the default from vip package is to take the last iteration as glmnet runs many iterations of lambda (penalty) by default too. Anyways thanks for this as always Julia!

[conlelevn]

Nice work Julia as always, sparse data is a new knowledge that I have not usually used before, if we did not apply this sparse data into our model, would it be any bad effect on the result? thanks

[juliasilge]

@conlelevn The main benefit of using a sparse representation is a speed-up in training in some situations. You can see an example of this here.