sklearn.model_selection
.LeavePOut¶

class
sklearn.model_selection.
LeavePOut
(p)[source]¶ LeavePOut crossvalidator
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 toKFold(n_splits=n_samples // p)
which creates nonoverlapping test sets.Due to the high number of iterations which grows combinatorically with the number of samples this crossvalidation method can be very costly. For large datasets one should favor
KFold
,StratifiedKFold
orShuffleSplit
.Read more in the User Guide.
Parameters:  p : int
Size of the test sets. Must be strictly greater 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 = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_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]
Methods
get_n_splits
(self, X[, y, groups])Returns the number of splitting iterations in the crossvalidator split
(self, X[, y, groups])Generate indices to split data into training and test set. 
get_n_splits
(self, X, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the crossvalidator
Parameters:  X : arraylike, 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.

split
(self, X, y=None, groups=None)[source]¶ Generate indices to split data into training and test set.
Parameters:  X : arraylike, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
 y : arraylike, of length n_samples
The target variable for supervised learning problems.
 groups : arraylike, 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.