# Do I need to standardize my one hot encoded labels?

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?

From what I can tell, there isn't a "right" answer to the title question. Most people I know wouldn't bother. (Indeed, one often-used scaler puts the data into the range $$[0,1]$$ anyway.) https://stats.stackexchange.com/questions/290929/standardizing-dummy-variables-for-variable-importance-in-glmnet