I guess this is very similar to the question that was highlighted a few weeks ago and I should caveat my question with that i am far from an expert.
I am reading
Applied Predictive Modelling by Dr Max Kuhn and Dr Kjell Johnson. I am currently reading the chapter in relation to feature selection. One of the points highlighted by the book is that feature selection takes predictors in isolation. What i mean by this is that items or features which have a low correlation with the item you are trying to predict or a high correlation with each other might be dropped as they are considered poor predictors in isolation. However there might be some interactions going on in the data-set that the researcher is not aware of where predictors in combination with each other could make excellent predictors. Theory should inform this of course and should be the primary driver especially during the data gathering stage but I suppose my questions are
If someone has a very large data-set with lots of predictors, Is there a modelling method that could point a researcher to investigate specific interactions.
Further to this, given interactions that this as yet unnamed modelling technique have highlighted, assuming the researcher has split training and test sets, Is it possible then to use these findings (generated model) in an inference capacity on the test set
Could someone direct me to some useful material on the subject (books, articles, blog posts).
I'm open to the very real possibility this may be a very novice question and might be complete nonsense .
Thanks very much for your time