I have a dataset containing 34 input columns and 8 output columns.One way to solve the problem is to take the 34 inputs and build individual regression model for each output column.I am wondering if this problem can be solved using just one model particularly using Neural Network.I have used Multilayer Perceptron but that needs multiple models just like linear regression. Can Sequence to Sequence be a viable option? I am using TensorFlow. I have code but I think it is more important to understand what I am missing out in terms of the multilayer perceptron theory. I understand that in MLP if you have one output node it will provide one output. If you have 10 output nodes then it is a multi class problem. You pick the class with the highest probability out of the 10 outputs. But in my case it is certain there will be 8 outputs for same input. Lets say, for a set of inputs you will get the 3D coordinate of something (X,Y,Z). Like, Inputs = {1,10,5,7} Output = {1,2,1}. So for the same input {1,10,5,7} I need to make models for X value Y value and Z. One solution is to have 3 different models using MLP. But I would like to see if I can have one model. So I thought about using seq2seq. Because the encoder takes a series of input and the decoder provides series of output. But it seems seq2seq in tensorflow cannot handle float values. I can be wrong about this though.
Neural Network for Multiple Output Regression
neural network;regression;tensorflow