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How is randomstate and random-state used in sklearn?
If you don’t set a seed, it is different each time. 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. Random state ensures that the splits that you generate are reproducible.
When to remove the random state argument in scikit-learn?
In practice I would say, you should set the random_state to some fixed number while you test stuff, but then remove it in production if you really need a random (and not a fixed) split. Not the answer you’re looking for? Browse other questions tagged linear-model scikit-learn or ask your own question.
How does random state number affect your model?
random_state number splits the test and training datasets with a random manner. In addition to what is explained here, it is important to remember that random_state value can have significant effect on the quality of your model (by quality I essentially mean accuracy to predict).
When to use the same random state for all datasets?
However, if you use a particular value for random_state (random_state = 1 or any other value) everytime the result will be same,i.e, same values in train and test datasets. when random_state set to an integer, train_test_split will return same results for each execution.
What happens if random _ state is none in Python?
In the documentation, it is stated that: If random_state is None or np.random, then a randomly-initialized RandomState object is returned. If random_state is an integer, then it is used to seed a new RandomState object.
How is random _ state = none splits randomly?
How ever random_state=None splits randomly each time. If you don’t specify the random_state in your 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.
Which is the random number generator in scikit learn?
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. source: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html