I am working with a lasso regression with the glmnet package. I read these threads: When conducting multiple regression, when should you center your predictor variables & when should you standardize them?, Need for centering and standardizing data in regression and Is standardisation before Lasso really necessary?.
Based on the responses I decided that I need to standardize my data before using it. I do have some questions however:
- Do I need to standardize the predictors and the responses or only the predictors?
- I am using the function scale(myData, center = TRUE, scale = TRUE) for building the model, but I am wondering what do I do when I want to do predictions with a test data set. I think I should also standardize and center the test data, but how to I do that? Substracting the mean from the initial (training) dataset and the dividing it by the standard deviation of the initial dataset?
- When I get a result do I need to "backscale" it (using the original mean and standard deviation) or do I already get the "final" result?