sklearn sk0.22-0.3.0 · OCaml Package

type tag = [

| `LeavePOut

]

type t = [ `BaseCrossValidator | `LeavePOut | `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 : int -> t

Leave-P-Out cross-validator

Provides train/test indices to split data in train/test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration.

Note: ``LeavePOut(p)`` is NOT equivalent to ``KFold(n_splits=n_samples // p)`` which creates non-overlapping test sets.

Due to the high number of iterations which grows combinatorically with the number of samples this cross-validation method can be very costly. For large datasets one should favor :class:`KFold`, :class:`StratifiedKFold` or :class:`ShuffleSplit`.

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

Parameters ---------- p : int Size of the test sets. Must be strictly less than the number of samples.

Examples -------- >>> import numpy as np >>> from sklearn.model_selection import LeavePOut >>> X = np.array([1, 2], [3, 4], [5, 6], [7, 8]) >>> y = np.array(1, 2, 3, 4) >>> lpo = LeavePOut(2) >>> lpo.get_n_splits(X) 6 >>> print(lpo) LeavePOut(p=2) >>> for train_index, test_index in lpo.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: 2 3 TEST: 0 1 TRAIN: 1 3 TEST: 0 2 TRAIN: 1 2 TEST: 0 3 TRAIN: 0 3 TEST: 1 2 TRAIN: 0 2 TEST: 1 3 TRAIN: 0 1 TEST: 2 3

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

Returns the number of splitting iterations in the cross-validator

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 : object Always ignored, exists for compatibility.

groups : object Always ignored, exists for compatibility.

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, 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, with shape (n_samples,), optional 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.

package sklearn