[D] How to approach a project with several sets(representations) of features?
I am working on a supervised regression problem and I have several different representations of essentially the same input features but with varying formulations and of varying length (model_i). I also have a list of properties that influence the target variable (props), which I am thinking of adding as a stacked layer. Basically I want to proceed in the following way:
model_i –> predict props –> predict target variable,
where I compute for each separate model “i” and just choose either the best performing result, or take the average over all initial models. Is there a better way to approach this problem? I was thinking of using decision tree methods with this approach. I am not too familiar with neural networks but it seems that they are usually ideal for image/video/audio tasks. Can someone point me in the right direction please. Thank you.