What Is "random-state" In Sklearn.model_selection.train_test_split Example?

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

Solution 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.

Solution 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.

Solution 3:

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.

Solution 4:

When the Random_state is not defined in the code for every run train data will change and accuracy might change for every run. When the Random_state = " constant integer" is defined then train data will be constant For every run so that it will make easy to debug.

Solution 5:

The random state is simply the lot number of the set generated randomly in any operation. We can specify this lot number whenever we want the same set again.