sklearn.model_selection
.LeavePOut¶
-
class
sklearn.model_selection.
LeavePOut
(p)[source]¶ 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 toKFold(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
KFold
,StratifiedKFold
orShuffleSplit
.Read more in the User Guide.
Parameters: p : int
Size of the test sets.
Examples
>>> 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
(X[, y, groups])Returns the number of splitting iterations in the cross-validator split
(X[, y, groups])Generate indices to split data into training and test set. -
get_n_splits
(X, y=None, groups=None)[source]¶ 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.
-
split
(X, y=None, groups=None)[source]¶ 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.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
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.
-