TidyTuesday: Feature Elimination with TidyModels

In this week's #TidyTuesday video, I go over common methods for handling data with a large number of correlated features. Using #TidyModels I go over general feature elimination methods using recipes. I then explain issues with correlated features and ways to analyze which correlated components to select. I explain different #MachineLearning algorithms that are useful for handling high-dimensional correlated data. I then show how to use domain knowledge and intuition by utilizing model variable importance scores. Finally, I show a brute force method of utilizing recursive feature elimination.
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TidyTuesday: github.com/rfo...
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[@prod.kashkari3075]

This gotta be the most underrated video ever. Mans dropped KNOWLEDGE

[@teegnas]

Woah ... this was some really awesome content ... so glad to have subscribed to your TidyTuesday project last year ... helped me a lot in my Data Science job ... thanks a lot, Andrew!

[@tighthead03]

great job on this video, there's so much info in here

[@rashawnhoward564]

Just finished reading the tidymodels book. This video is a great edition.

[@Rodr51zx]

Hey, can you send me the link for the book?

[@rashawnhoward564]

@@Rodr51zx The link is: www.tmwr.org I made a pdf version so I could read it on my kindle

[@mattm9069]

This is awesome. Thank you! Have you tried building a random forest without dummy encoding? I'm curious about the model performance in that case. Apparently, the ranger package can handle the raw columns. On the other hand, xgboost needs the dummy encoding.

[@AndrewCouch]

I have done it without dummying/one-hot encoding and it generally will not make a difference. For me, I like to do dummy encoding so I can try different models with the same recipe. Thanks for watching!

[@邵扬]

welcome back!

[@517127]

O miss your videos