My Training dataset is huge. That is why It is giving a memory allocation error.
I found that I might try a few things:
a) Use xgb_model parameter - file name of stored XGBoost model or ‘Booster’ instance XGBoost model to be loaded before training (allows training continuation).
(But NOT Sure - fitting multiple times will give the same results compare to single time)
b) DeviceQuantileDMatrix on a single GPU
c) Distributed - Ray or Dask
Could anyone share your experience about - is it possible to get the same results with a, b, and c compared to a trained model using whole data at once.?
Among these three options (a, b, c), which one will be the best option?