scikit-learn random state in splitting dataset

Question

Can anyone tell me why we set random state to zero in splitting train and test set.

X_train, X_test, y_train, y_test = \\
    train_test_split(X, y, test_size         


        

Answer 1

when random_state set to an integer, train_test_split will return same results for each execution.

when random_state set to an None, train_test_split will return different results for each execution.

see below example:

from sklearn.model_selection import train_test_split

X_data = range(10)
y_data = range(10)

for i in range(5):
    X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.3,random_state = 0) # zero or any other integer
    print(y_test)

print("*"*30)

for i in range(5): 
    X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size = 0.3,random_state = None)
    print(y_test)

Output:

[2, 8, 4]

[2, 8, 4]

[2, 8, 4]

[2, 8, 4]

[2, 8, 4]

---

[4, 7, 6]

[4, 3, 7]

[8, 1, 4]

[9, 5, 8]

[6, 4, 5]