What is "random-state" in sklearn.model_selection.train_test_split example? [duplicate]

Question

Can someone explain me what random_state means in below example?

import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)


X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42) 

Why is it hard coded to 42?

Answer 1

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.

Relevant documentation:

random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Answer 2

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.