How to choose RandomState in train_test_split?
For me personally, I set random_state to a specific number (usually 42) so if I see variation in my programs accuracy I know it was not caused by how the data was split.
However, this can lead to my network over fitting on that specific split.  I.E. I tune my network so it works well with that split, but not necessarily on a different split.  Because of this, I think it's best to use a random seed when you submit your code so the reviewer knows you haven't over fit to that particular state.
To do this with sklearn.train_test_split you can simply not provide a random_state and it will pick one randomly using np.random.
I understand how random state is used to randomly split data into training and test set. As Expected, my algorithm gives different accuracy each time I change it. Now I have to submit a report in my university and I am unable to understand the final accuracy to mention there. Should I choose the maximum accuracy I get? Or should I run it with different RandomStates and then take its average? Or something else?