[D] Please critique my ML method.
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.