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[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.

submitted by /u/xk86
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.