What is “random-state” in sklearn.model_selection.

问题:

I am really new to machine learning,i was going through some example on sklearn

Can someone explain me what really "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

list(y)


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

X_train

y_train

X_test

y_test

Why its hard coded to 42?

回答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.

回答2:

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.