I might have a silly question - I am building a linear model with many many attributes. I have narrow down to a few - I do have a group of 3 attributes that are highly correlated (for example sales amount for the past 1 years, 2 years and 3 years). I don't want to only keep one of them and exclude the rest.
Can I build a decision tree of just those 3 attributes against the target, and based on the tree results ( the rules) and create a new attribute combing those 3? so it will be a binned attributes based on the tree nodes.
it is very predictive and utilized all 3 original attributes. I am wondering whether there is anything major wrong with doing this? I cannot find anyone doing things like this.