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- train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)
- Split arrays or matrices into random train and test subsets.
Parameters
----------
*arrays : sequence of arrays
Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas DataFrames.
test_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples.
train_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If int,
represents the absolute number of train samples. If None, the value is
automatically computed as the complement of the test size (unless both
are None, in which case test_size defaults to 0.25).
random_state : int, RandomState instance or None, default=None
Controls the shuffling applied to the data before applying the split.
Pass an int for reproducible output across multiple function calls.
shuffle : bool, default=True
Whether or not to shuffle the data before splitting. If shuffle=False
then stratify must be None.
stratify : array-like, default=None
If not None, data is split in a stratified fashion, using this as
the class labels.
Returns:
-------
splitting : list, length=2 * len(arrays)
List containing train-test split of inputs.
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