package sklearn

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package sklearn

sklearn
- Sklearn

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type tag = [

| `BaseCrossValidator

]

type t = [ `BaseCrossValidator | `Object ] Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

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

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) 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,) 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 : Format.formatter -> t -> unit

Pretty-print the object to a formatter.