Let's say I have built a boosting tree or neural network and I standardized my features beforehand. When I built my model, I split my data into training, validation, and test sets - each with their own means and standard deviations for normalization. This is based on the recommendation of a book, which says it is important not to mix your data, even when it comes to standardization.
Anyways, let's say I have a final model. Now, I have a new observation and I want to predict the outcome.
What do I do with the new observation? The feature values are obviously not standardized. Since it is one data point, it doesn't have a standard deviation. Are there any issues with prediction if I feed this new single observation into my model? The scaling would be completely off from the ones I fed into the model.
If I need to standardize it, what should my mean and standard deviation be?