package sklearn

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package sklearn
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type tag = [
  1. | `GroupKFold
]
type t = [ `BaseCrossValidator | `GroupKFold | `Object ] 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 -> unit -> t

K-fold iterator variant with non-overlapping groups.

The same group will not appear in two different folds (the number of distinct groups has to be at least equal to the number of folds).

The folds are approximately balanced in the sense that the number of distinct groups is approximately the same in each fold.

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.

Examples -------- >>> import numpy as np >>> from sklearn.model_selection import GroupKFold >>> X = np.array([1, 2], [3, 4], [5, 6], [7, 8]) >>> y = np.array(1, 2, 3, 4) >>> groups = np.array(0, 0, 2, 2) >>> group_kfold = GroupKFold(n_splits=2) >>> group_kfold.get_n_splits(X, y, groups) 2 >>> print(group_kfold) GroupKFold(n_splits=2) >>> for train_index, test_index in group_kfold.split(X, y, groups): ... print('TRAIN:', train_index, 'TEST:', test_index) ... X_train, X_test = Xtrain_index, Xtest_index ... y_train, y_test = ytrain_index, ytest_index ... print(X_train, X_test, y_train, y_test) ... TRAIN: 0 1 TEST: 2 3 [1 2] [3 4] [5 6] [7 8] 1 2 3 4 TRAIN: 2 3 TEST: 0 1 [5 6] [7 8] [1 2] [3 4] 3 4 1 2

See also -------- LeaveOneGroupOut For splitting the data according to explicit domain-specific stratification of the dataset.

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 : ?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

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.

y : array-like of shape (n_samples,), default=None The target variable for supervised learning problems.

groups : array-like of shape (n_samples,) 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.