This is a more theoretical question, perhaps less suiting to the required format of a question on cross validated. Still I hope someone can help or advise me.
I'm using r.
I have a data frame with roughly 180k observations and 120 variables. When running through a multiple regression model there ends up being ~1000 variables including dummy variables.
My baseline kitchen sink model is the target variable as a function of all ~999 variables. This takes r about 10 minutes to compute on a single 0.8 train and 0.2 test data on my 6gb Mac, 2.2 processor.
On my baseline kitchen sink model I also did kfolds cross validation on training with 10 folds. Roughly this took an hour to compute.
I'm looking at my notes from school on feature selection. I would like to try stepwise regression, forwards and backwards to see what it comes with for my performance measure Mean Absolute Error.
I would also, for each iteration within stepwise, train using cross validation.
Is this possible? If running the baseline model 10 times with a single cross validation takes 1hour then I'm about to ask r to do the same 999 times with a stepwise instruction.
Is what I'm trying to do "normal"? Is my understanding and approach "sane"? Given the size of my data and that I'm on a 6gb Mac, what would be a logical approach for feature selection and training?
If I should provide any more info here such as
str(df) let me know.