Balancing dataset and normalizing features: what comes first? I have a unbalanced data set, that for the purpose of training I wish to balance. What is a better practice? First balancing the train data and then feature scaling/normalizing or using the mean and standard deviation of the skewed set?
Thanks for your help! =)
 A: Are you talking about a predictive model? I will assume yes. Also assume you are talking about balancing the target variable for rare class classification tasks. My thought would be to standardize (normalizing is typically using the min and max values not mean and standard deviation) the data first and then over-sample if that is what you are thinking in terms of balancing. I say this because you will want to use that same mean/std dev of the original set when you standardize new data so that it mirrors the training set that was used. The suggestion above not to balance is not true, it really depends on how rare the rare class is and the technique. Often, machine learning algorithms work better with closer to balance data (either explicitly or through use of weights).
A: 
I have a unbalanced data set, that for the purpose of training I wish to balance.

By which you mean, throw out or duplicate observations? This is unwise. It will only make your model worse.
So, just normalize and forget about balancing.
