# Combining categorical and continuous features for neural networks

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?

• – Sycorax
Aug 1 '20 at 15:31
• – Sycorax
Aug 1 '20 at 15:31