package sklearn

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package sklearn
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type tag = [
  1. | `StratifiedKFold
]
type t = [ `BaseCrossValidator | `Object | `StratifiedKFold ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_cross_validator : t -> [ `BaseCrossValidator ] Obj.t
val create : ?n_splits:int -> ?shuffle:bool -> ?random_state:int -> unit -> t

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 :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 : bool, default=False Whether to shuffle each class's samples before splitting into batches. Note that the samples within each split will not be shuffled.

random_state : int or RandomState instance, default=None When `shuffle` is True, `random_state` affects the ordering of the indices, which controls the randomness of each fold for each class. Otherwise, leave `random_state` as `None`. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

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 -> [> tag ] Obj.t -> int

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.

val split : ?groups:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> y:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> ([> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t) Stdlib.Seq.t

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,) 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 -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.