sklearn.model_selection
.ShuffleSplit¶

class
sklearn.model_selection.
ShuffleSplit
(n_splits=10, test_size=0.1, train_size=None, random_state=None)[source]¶ Random permutation crossvalidator
Yields indices to split data into training and test sets.
Note: contrary to other crossvalidation strategies, random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.
Read more in the User Guide.
Parameters: n_splits : int (default 10)
Number of reshuffling & splitting iterations.
test_size : float, int, or None, default 0.1
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.
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, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Examples
>>> from sklearn.model_selection import ShuffleSplit >>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) >>> y = np.array([1, 2, 1, 2]) >>> rs = ShuffleSplit(n_splits=3, test_size=.25, random_state=0) >>> rs.get_n_splits(X) 3 >>> print(rs) ShuffleSplit(n_splits=3, random_state=0, test_size=0.25, train_size=None) >>> for train_index, test_index in rs.split(X): ... print("TRAIN:", train_index, "TEST:", test_index) ... TRAIN: [3 1 0] TEST: [2] TRAIN: [2 1 3] TEST: [0] TRAIN: [0 2 1] TEST: [3] >>> rs = ShuffleSplit(n_splits=3, train_size=0.5, test_size=.25, ... random_state=0) >>> for train_index, test_index in rs.split(X): ... print("TRAIN:", train_index, "TEST:", test_index) ... TRAIN: [3 1] TEST: [2] TRAIN: [2 1] TEST: [0] TRAIN: [0 2] TEST: [3]
Methods
get_n_splits
([X, y, groups])Returns the number of splitting iterations in the crossvalidator split
(X[, y, groups])Generate indices to split data into training and test set. 
get_n_splits
(X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
Parameters: X : object
Always ignored, exists for compatibility.
y : object
Always ignored, exists for compatibility.
groups : object
Always ignored, exists for compatibility.
Returns: n_splits : int
Returns the number of splitting iterations in the crossvalidator.

split
(X, y=None, groups=None)[source]¶ Generate indices to split data into training and test set.
Parameters: X : arraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
y : arraylike, shape (n_samples,)
The target variable for supervised learning problems.
groups : arraylike, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
