package sklearn

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package sklearn
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type tag = [
  1. | `RepeatedKFold
]
type t = [ `Object | `RepeatedKFold ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val create : ?n_splits:int -> ?n_repeats:int -> ?random_state:int -> unit -> t

Repeated K-Fold cross validator.

Repeats K-Fold n times with different randomization in each repetition.

Read more in the :ref:`User Guide <cross_validation>`.

Parameters ---------- n_splits : int, default=5 Number of folds. Must be at least 2.

n_repeats : int, default=10 Number of times cross-validator needs to be repeated.

random_state : int or RandomState instance, default=None Controls the randomness of each repeated cross-validation instance. 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 RepeatedKFold >>> X = np.array([1, 2], [3, 4], [1, 2], [3, 4]) >>> y = np.array(0, 0, 1, 1) >>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124) >>> for train_index, test_index in rkf.split(X): ... print('TRAIN:', train_index, 'TEST:', test_index) ... X_train, X_test = Xtrain_index, Xtest_index ... y_train, y_test = ytrain_index, ytest_index ... TRAIN: 0 1 TEST: 2 3 TRAIN: 2 3 TEST: 0 1 TRAIN: 1 2 TEST: 0 3 TRAIN: 0 3 TEST: 1 2

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.

See also -------- RepeatedStratifiedKFold: Repeats Stratified K-Fold n times.

val get_n_splits : ?x:Py.Object.t -> ?y:Py.Object.t -> ?groups:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> int

Returns the number of splitting iterations in the cross-validator

Parameters ---------- X : object Always ignored, exists for compatibility. ``np.zeros(n_samples)`` may be used as a placeholder.

y : object Always ignored, exists for compatibility. ``np.zeros(n_samples)`` may be used as a placeholder.

groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set.

Returns ------- n_splits : int Returns the number of splitting iterations in the cross-validator.

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

Generates 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.

y : array-like of length n_samples The target variable for supervised learning problems.

groups : array-like of shape (n_samples,), default=None Group labels for the samples used while splitting the dataset into train/test set.

Yields ------ train : ndarray The training set indices for that split.

test : ndarray The testing set indices for that split.

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.