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Creating training and test sets

When a dataset is large enough, it's a good practice to split it into training and test sets; the former to be used for training the model and the latter to test its performances. In the following figure, there's a schematic representation of this process:

There are two main rules in performing such an operation:

Both datasets must reflect the original distribution
The original dataset must be randomly shuffled before the split phase in order to avoid a correlation between consequent elements

With scikit-learn, this can be achieved using the train_test_split() function:

from sklearn.model_selection import train_test_split

>>> X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=1000)

The parameter test_size (as well as training_size) allows specifying the percentage of elements to put into the test/training set. In this case, the ratio is 75 percent for training and 25 percent for the test phase. Another important parameter is random_state which can accept a NumPy RandomState generator or an integer seed. In many cases, it's important to provide reproducibility for the experiments, so it's also necessary to avoid using different seeds and, consequently, different random splits:

My suggestion is to always use the same number (it can also be 0 or completely omitted), or define a global RandomState which can be passed to all requiring functions.

from sklearn.utils import check_random_state

>>> rs = check_random_state(1000)
<mtrand.RandomState at 0x12214708>

>>> X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=rs)

In this way, if the seed is kept equal, all experiments have to lead to the same results and can be easily reproduced in different environments by other scientists.

For further information about NumPy random number generation, visit https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.RandomState.html.