I have a dataset of 300 000 rows and an ensemble model, which include grid search to find the best params of every algorithm. Unfortunately the grid search needs to long and I have problems to implement gpu running for the different algorithms (xgb,lightgbm..). Also the models which are from scikit-learn like random-forest dont run on gpu. The idea is now, instead of 300 000 rows, I will create a small dataset with max. 500 rows, which need less time than the full dataset.
Could the minimal required sample size calculation help?
How big should the sample be? to get a good data distribution of the big dataset?