Evaluate multiple ML approaches for spam detection

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Julia Silge

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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.

MrAbhimufc

I always learn so much from your tutorial videos! It's exciting to watch the tidymodels framework becoming more and more fleshed out.

tiernanmartin

Thank you very much for this screencast, I hope to see your lecteur twice a month, again, many thanks .

cheninitayeb

Many thanks for yet another great lecture!

darmaw

Great video. Thanks for the clarification on random forests behavior regarding tuning and trees.

HM-woic

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.

atlantaguitar

Can you please go over the time_split method for time-series data ?

matthewschorr

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?

Jakan-sfxj

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?

hesamseraj

Evaluate multiple ML approaches for spam detection

Evaluate multiple ML approaches for spam detection

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