I have a time series data that handled using GDBT to predict the next value. I always use previous 30 days data to train daily, but overtime the data to predict and train is increased because the number of combination things increased.
My question is, how often do we need to hypertune our model? and what eval number combination considered to be enough for hyperparameter tuning? 50? 100? or just 10 is enough?
right now I do it daily, but it getting costly and costly, previously it just aroung 10 minutes, but now about more than hour. The system need to do other things hourly so this hypertuning model will be a deadlock for the system.