Is it OK to combine categorical and continuous features into the same vector for training deep neural networks? Say there is a categorical feature and continuous feature that I want to feed into a deep neural net at the same time. Is this the way to do it?
categorical feature (one-hot encoded) = [0,0,0,1,0] continuous feature (number) = 8 final feature vector passed into neural network = categorical feature vector CONCATENATE continuous feature = [0,0,0,1,0,8]
Basically, the question is, is it OK to have a one-hot encoding and a continuous feature together in one feature vector?