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[D] Please critique my ML method.

Hello all,

My data points consist of 71 attributes, each numeric between 0 and 1, the target class is binary and my belief is the relationship is non linear. In addition, the attributes have a 1D spacial relation to each other, this may or may not be important.

I would like to have a model to be able to classify each tuple, but equally as important to me is a way to understand which attributes are the most weightful in the classification.

I’m thinking I should use an autoencoder for the second part, and to use the output from that as an input for a model, such as another NN.

Does that sound right, or am I leaning too heavily in the direction of NNs because of how my data looks?

Thank you all very much in advance.

submitted by /u/guyshur
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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.