Split arrays or matrices into random train and test subsets
Deprecated since version 0.18: This module will be removed in 0.20. Use
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.
Read more in the User Guide.
*arrays : sequence of indexables with same length / shape
Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.
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.
stratify : array-like or None (default is None)
If not None, data is split in a stratified fashion, using this as the labels array.
New in version 0.17: stratify splitting
splitting : list, length = 2 * len(arrays),
List containing train-test split of inputs.
New in version 0.16: If the input is sparse, the output will be a
scipy.sparse.csr_matrix. Else, output type is the same as the input type.
>>> import numpy as np >>> from sklearn.cross_validation import train_test_split >>> X, y = np.arange(10).reshape((5, 2)), range(5) >>> X array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) >>> list(y) [0, 1, 2, 3, 4]
>>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.33, random_state=42) ... >>> X_train array([[4, 5], [0, 1], [6, 7]]) >>> y_train [2, 0, 3] >>> X_test array([[2, 3], [8, 9]]) >>> y_test [1, 4]