I don't know which one is the better way to do. I use
scikit to train ML and save it.
I train Model
M with train data
X and test data
y, then save it to predict in next time.
When I get new observation data
X_new, how should I retrain with
X_new? (Assume the
X_new is normal distribution but size is half of
I don't know is it ok to retrain only with
X_new or need to retrain with
I know loading
M and train X+X_new is the best way.
But if I lose train data X? That's what I concerned most.
Is it effective retraining only with
X_new? the previous coef in
M will be affected?