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# `sklearn.model_selection`.StratifiedShuffleSplit

class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)

  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.

  For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn

  Parameters:
    n_splits int, default=10

      Number of re-shuffling & splitting iterations.

    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. If None, the value is set to the complement of the train size. If `train_size` is also None, it will be set to 0.1.

    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 set to the complement of the test size.

    random_state int, RandomState instance or None, default=None

      Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.

  Examples

      >>> import numpy as np
      >>> from sklearn.model_selection import StratifiedShuffleSplit
      >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
      >>> y = np.array([0, 0, 0, 1, 1, 1])
      >>> sss = StratifiedShuffleSplit(n_splits=5, test_size=0.5, random_state=0)
      >>> sss.get_n_splits(X, y)
      5
      >>> print(sss)
      StratifiedShuffleSplit(n_splits=5, random_state=0, ...)
      >>> for i, (train_index, test_index) in enumerate(sss.split(X, y)):
      ...     print(f"Fold {i}:")
      ...     print(f"  Train: index={train_index}")
      ...     print(f"  Test:  index={test_index}")
      Fold 0:
        Train: index=[5 2 3]
        Test:  index=[4 1 0]
      Fold 1:
        Train: index=[5 1 4]
        Test:  index=[0 2 3]
      Fold 2:
        Train: index=[5 0 2]
        Test:  index=[4 3 1]
      Fold 3:
        Train: index=[4 1 0]
        Test:  index=[2 3 5]
      Fold 4:
        Train: index=[0 5 1]
        Test:  index=[3 4 2]

  Methods

  `get_metadata_routing`() | Get metadata routing of this object.
  `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.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_n_splits(X=None, y=None, groups=None)

    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.

  split(X, y, groups=None)

    Generate indices to split data into training and test set.

    Parameters:
      X array-like of 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 hence `np.zeros(n_samples)` may be used as a placeholder for `X` instead of actual training data.

      y array-like of shape (n_samples,) or (n_samples, n_labels)

        The target variable for supervised learning problems. Stratification is done based on the y labels.

      groups object

        Always ignored, exists for compatibility.

    Yields:
      train ndarray

        The training set indices for that split.

      test ndarray

        The testing set indices for that split.

    Notes

    Randomized CV splitters may return different results for each call of split. You can make the results identical by setting `random_state` to an integer.

## Examples using `sklearn.model_selection.StratifiedShuffleSplit`

Visualizing cross-validation behavior in scikit-learn

Visualizing cross-validation behavior in scikit-learn

RBF SVM parameters

RBF SVM parameters