#scikit

Building and optimizing pipelines in Scikit-Learn ☞ https://morioh.com/p/a49e85477c45?f=5c21fb01c16e2556b555ab32

Learning Model Building in Scikit-learn : A Python Machine Learning Library https://morioh.com/p/5d495b41c153

Learning Model Building in Scikit-learn : A Python Machine Learning Library https://morioh.com/p/5d495b41c153

original source : https://stackoverflow.com/questions/28064634/random-state-pseudo-random-number-in-scikit-learn

train_test_split splits arrays or matrices into random train and test subsets. That means that everytime you run it without specifying random_state, you will get a different result, this is expected behavior. For example:

Run 1:

>>> a, b = np.arange(10).reshape((5, 2)), range(5) >>> train_test_split(a, b) [array([[6, 7],        [8, 9],        [4, 5]]), array([[2, 3],        [0, 1]]), [3, 4, 2], [1, 0]]

Run 2

>>> train_test_split(a, b) [array([[8, 9],        [4, 5],        [0, 1]]), array([[6, 7],        [2, 3]]), [4, 2, 0], [3, 1]]

It changes. On the other hand if you use random_state=some_number, then you can guarantee that the output of Run 1 will be equal to the output of Run 2, i.e. your split will be always the same. It doesn't matter what the actual random_statenumber is 42, 0, 21, ... The important thing is that everytime you use 42, you will always get the same output the first time you make the split. This is useful if you want reproducible results, for example in the documentation, so that everybody can consistently see the same numbers when they run the examples. 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.