val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_splits:int ->
?shuffle:bool ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
unit ->
tStratified 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 :ref:`User Guide <cross_validation>`.
Parameters ---------- n_splits : int, default=5 Number of folds. Must be at least 2.
.. versionchanged:: 0.22 ``n_splits`` default value changed from 3 to 5.
shuffle : boolean, optional Whether to shuffle each class's samples before splitting into batches.
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`. Only used when ``shuffle`` is True. This should be left to None if ``shuffle`` is False.
Examples -------- >>> import numpy as np >>> 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 = Xtrain_index, Xtest_index ... y_train, y_test = ytrain_index, ytest_index TRAIN: 1 3 TEST: 0 2 TRAIN: 0 2 TEST: 1 3
Notes ----- The implementation is designed to:
* Generate test sets such that all contain the same distribution of classes, or as close as possible. * Be invariant to class label: relabelling ``y = "Happy", "Sad"`` to ``y = 1, 0`` should not change the indices generated. * Preserve order dependencies in the dataset ordering, when ``shuffle=False``: all samples from class k in some test set were contiguous in y, or separated in y by samples from classes other than k. * Generate test sets where the smallest and largest differ by at most one sample.
.. versionchanged:: 0.22 The previous implementation did not follow the last constraint.
See also -------- RepeatedStratifiedKFold: Repeats Stratified K-Fold n times.
val get_n_splits :
?x:Py.Object.t ->
?y:Py.Object.t ->
?groups:Py.Object.t ->
t ->
intReturns 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.
val split :
?groups:Py.Object.t ->
x:Ndarray.t ->
y:Ndarray.t ->
t ->
Py.Object.tGenerate 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 hence ``np.zeros(n_samples)`` may be used as a placeholder for ``X`` 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.
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.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.