I'm trying to do a simple softmax regression where I have features (2 columns) and a one hot encoded vector of labels (two categories: left = 1 and Right = 0). Do I need to standardize just the vector of features or also the vector of labels? when I do that all my zeros and ones transform in different numbers and also I don't know how to identify who is the Left or the Right category. I'm using mxnet and gluon. Here is how I standardize: labels = (labels - labels.mean()) / (labels.max() - labels.min())
labels before standardization: [0. 1. 1. 1. 1. 1.
labels after standardization: [-0.5633803 0.43661973 0.43661973 0.43661973 0.43661973 0.43661973 ...
How can I after identify (with strings) if my prediction is actually giving me Left or Right?