What is "random-state" in sklearn.model_selection.train_test_split example?
Isn't that obvious? 42 is the Answer to the Ultimate Question of Life, the Universe, and Everything.
On a serious note,
random_state simply sets a seed to the random generator, so that your train-test splits are always deterministic. If you don't set a seed, it is different each time.
None, optional (default=
random_stateis the seed used by the random number generator; If
random_stateis the random number generator; If
None, the random number generator is the
RandomStateinstance used by
Random state ensures that the splits that you generate are reproducible. Scikit-learn uses random permutations to generate the splits. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.
If you don't specify the random_state in the code, then every time you run(execute) your code a new random value is generated and the train and test datasets would have different values each time.
However, if a fixed value is assigned like random_state = 0 or 1 or 42 or any other integer then no matter how many times you execute your code the result would be the same .i.e, same values in train and test datasets.