I used StandardScaler provided by scikit-learn to scale training and validation data. Then, I fitted a neural network (CNN) model with scaled data for classification. However, in the production stage, I have to predict data in every month in the future (one-by-one). So, I use the scaler which was used in training stage to fit the new data.
My training procedure as follows:
sc = new StandardScaler() sc.fit(train_data) train_data = sc.transform(train_data) build model, save(sc)
For prediction steps:
sc.fit(predict_data) sc.transform(predict_data) model.predict(predict_data)
There is no information about min/max in my data. I think in the prediction stage, mean and stdev of data will be changed. What should I do in this case to predict new data?