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class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)
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Stratified ShuffleSplit cross-validator
Provides train/test indices to split data in train/test sets.
This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.
Note: like the ShuffleSplit strategy, stratified 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 re-shuffling & splitting iterations.
test_size : float (default 0.1), int, or 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.
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.
Examples
>>> from sklearn.model_selection import StratifiedShuffleSplit >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> sss = StratifiedShuffleSplit(n_splits=3, test_size=0.5, random_state=0) >>> sss.get_n_splits(X, y) 3 >>> print(sss) StratifiedShuffleSplit(n_splits=3, random_state=0, ...) >>> for train_index, test_index in sss.split(X, y): ... print("TRAIN:", train_index, "TEST:", test_index) ... X_train, X_test = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_index] TRAIN: [1 2] TEST: [3 0] TRAIN: [0 2] TEST: [1 3] TRAIN: [0 2] TEST: [3 1]
Methods
get_n_splits
([X, y, groups])Returns the number of splitting iterations in the cross-validator split
(X, y[, groups])Generate indices to split data into training and test set. -
__init__(n_splits=10, test_size=0.1, train_size=None, random_state=None)
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get_n_splits(X=None, y=None, groups=None)
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Returns the number of splitting iterations in the cross-validator
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 cross-validator.
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split(X, y, groups=None)
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Generate indices to split data into training and test set.
Parameters: X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
Note that providing
y
is sufficient to generate the splits and hencenp.zeros(n_samples)
may be used as a placeholder forX
instead of actual training data.y : array-like, shape (n_samples,)
The target variable for supervised learning problems. Stratification is done based on the y labels.
groups : object
Always ignored, exists for compatibility.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
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model_selection.StratifiedShuffleSplit()
Examples using
2017-01-15 04:24:20
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