Hey Jeremy, fantastic lessons. Looking forward to working through part 2! Quick question: When working with Random Forests and XGBoost regressors/classifiers, is there ever any accuracy advantage to using ordinal encoding over one-hot encoding? I realize there can be speed advantages when the number of categories in a column grows large, but if speed isn't a factor, do we even have a reason to play around with ordinal encoding? (Sorry if this was answered somewhere and I missed it!)
Yes, there can be a difference in some circumstances. Ordinal encoding assumes that there is an inherent ordering to the categories which may not have any meaning in real life. For example, mapping "Bad", "Acceptable" & "Good" to 1, 2 & 3 makes sense, but mapping "Irish", "Scottish" & "Welch" to an ordinal list doesn't make much sense in most contexts. Some models will be more or less sensitive to factors like this. When in doubt, try both and see which works better.
At about 35 mins in you jumped to some notebook that I can't find and you don't explain where it is. The written text is much clearer than the verbal explanations. It would be really helpful if your notebooks were numbered corresponding to the lessons. E.g. "Lesson 5b: Why you should use a framework"
Jeremy, these videos are brilliant. Thank you so much for creating them. I've heard you mention the zoom walkthrough videos a few times. Are they available to watch anywhere?
