train test data split up in datasets In a dataset consisting of 1,000 samples, it has been shown that a 70-30 split
(i.e. 70% of the samples used for training, 30% for validation) will provide a good estimation
of the test accuracy of the trained models. If the dataset size increases to 10,000 samples, what
split would you suggest?
The current approach use 70/30 or 80/20, the most used is 80/20 (train/test). However there is other things you should check, for example if you data is balanced. If your data is not balanced you might want to use undersample or oversample.