sklearn.model_selection.StratifiedShuffleSplit  
class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) [source]
 
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_splitsint, default=10 

Number of re-shuffling & splitting iterations.  
test_sizefloat 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_sizefloat 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_stateint, 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 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: [5 2 3] TEST: [4 1 0]
TRAIN: [5 1 4] TEST: [0 2 3]
TRAIN: [5 0 2] TEST: [4 3 1]
TRAIN: [4 1 0] TEST: [2 3 5]
TRAIN: [0 5 1] TEST: [3 4 2]
 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.    
get_n_splits(X=None, y=None, groups=None) [source]
 
Returns the number of splitting iterations in the cross-validator  Parameters 
 
Xobject 

Always ignored, exists for compatibility.  
yobject 

Always ignored, exists for compatibility.  
groupsobject 

Always ignored, exists for compatibility.    Returns 
 
n_splitsint 

Returns the number of splitting iterations in the cross-validator.     
  
split(X, y, groups=None) [source]
 
Generate indices to split data into training and test set.  Parameters 
 
Xarray-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.  
yarray-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.  
groupsobject 

Always ignored, exists for compatibility.    Yields 
 
trainndarray 

The training set indices for that split.  
testndarray 

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  

  RBF SVM parameters