Python random state in splitting dataset

To solve the train test split random state, you should go through the answer further:

Random_state can be 0 or 1 or any other integer. It should be the same value if you want to validate your processing over multiple runs of the code. By the way, I have seen random_state=42 used in various official examples of scikit.

the random_state parameter is used for starting the internal random number generator, which will decide the splitting of data into train and test indices in your case.

If random_state is None or np. random, then a randomly initialized RandomState object is returned.

If random_state is an integer, then it is used to seed a new RandomState object.

This is to check and validate the data when running the code multiple times. Setting random_state a fixed value will guarantee that the same sequence of random numbers is generated each time you run the code.