sklearn.cross_validation.train_test_split¶
- sklearn.cross_validation.train_test_split(*arrays, **options)[source]¶
Split arrays or matrices into random train and test subsets
Quick utility that wraps input validation and next(iter(ShuffleSplit(n_samples))) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.
Parameters: *arrays : sequence of arrays or scipy.sparse matrices with same shape[0]
Python lists or tuples occurring in arrays are converted to 1D numpy arrays.
test_size : float, int, or None (default is 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. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25.
train_size : float, int, or None (default is 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 set to the complement of the test size.
random_state : int or RandomState
Pseudo-random number generator state used for random sampling.
Returns: splitting : list of arrays, length=2 * len(arrays)
List containing train-test split of input array.
Examples
>>> import numpy as np >>> from sklearn.cross_validation import train_test_split >>> a, b = np.arange(10).reshape((5, 2)), range(5) >>> a array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) >>> list(b) [0, 1, 2, 3, 4]
>>> a_train, a_test, b_train, b_test = train_test_split( ... a, b, test_size=0.33, random_state=42) ... >>> a_train array([[4, 5], [0, 1], [6, 7]]) >>> b_train [2, 0, 3] >>> a_test array([[2, 3], [8, 9]]) >>> b_test [1, 4]
