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class sklearn.model_selection.StratifiedKFold(n_splits=3, shuffle=False, random_state=None)
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Stratified K-Folds cross-validator
Provides train/test indices to split data in train/test sets.
This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.
Read more in the User Guide.
Parameters: n_splits : int, default=3
Number of folds. Must be at least 2.
shuffle : boolean, optional
Whether to shuffle each stratification of the data before splitting into batches.
random_state : None, int or RandomState
When shuffle=True, pseudo-random number generator state used for shuffling. If None, use default numpy RNG for shuffling.
Notes
All the folds have size
trunc(n_samples / n_splits)
, the last one has the complementary.Examples
>>> from sklearn.model_selection import StratifiedKFold >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> skf = StratifiedKFold(n_splits=2) >>> skf.get_n_splits(X, y) 2 >>> print(skf) StratifiedKFold(n_splits=2, random_state=None, shuffle=False) >>> for train_index, test_index in skf.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 3] TEST: [0 2] TRAIN: [0 2] TEST: [1 3]
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=3, shuffle=False, 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.StratifiedKFold()
Examples using
2017-01-15 04:24:19
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