I do understand the concept of normalizing & scaling the training/test data; it does help with the converging of the cost function. It is a great helper for many of the machine learning algorithms.
- I train and validate my model with the normalized data (MinMaxScaler) and save my model.
- A new input data comes in and I want to use my saved model to make predictions.
- I no longer have access to the training/test data at this point. All I have is the new single row of input data.
- How will I normalize this single vector of input data so that it can be fed to my model?
The only normalization that I can think of simply linear transformation of data (e.g. simply map values from [40, 70] range to [-1, 1]). I need a normalization technique that doesn't depend on the full range of data.