Logistic regression for US House election vote share 

Thanks for the tip on using type.predict = "response" in the broom::augment function. I learn something new from each of your vides, so thanks for the work that you put into making these.

Amazing analysis! :O

Awesome, thank you for the tip for showing what a model doing with a fake new data with crossing and augment!

Hello, awesome content as usual , can you do a deep learning model for the next video, it would be great how to create a complicated NN , and add layers or use semi-supervised learning using tidymodels !

Dear Julia, I was trying to find an example of tidymodels usage for time series forecasting (I want to create my own pipeline that can be used for any time series data as a first step, just run and see what happens). I am interested in cross-validation options for time series that tidymodels offers (probably inherits something from caret). Do you have any videos on that (I failed to find appropriate one)? Maybe you can share some links with code examples? I expect that you have touched this issue previously when demonstrating tidymodels power. Another problem I encountered was the construction of forecasts for future dates with a model that uses lagged dependent variables as predictors: is there a routine in tidymodels or somewhere else that allows one to automatically generate appropriate data for future predictions (say, if I want to predict for t+2, my lagged dep variable will be the prediction for t+1)? I had to code my own manipulations with a loop (create new row, then fill in values of necessary variables, then pass it to predict(model, newdata = this_new_row)), I am sure there are optimal solutions for this issue, maybe you are familiar with one (part of this problem was that among variables I also had an indicator for whether the day is a weekend or not, there was a dplyr pipeline I used to create it, is there a way to tell tidymodels "look at this pipeline, that is how I create all predictors for my model, please use this pipeline for predict()").