sklearn.model_selection.LeavePOut — scikit-learn 0.20.4 documentation (original) (raw)
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, StratifiedKFoldor ShuffleSplit.
Read more in the User Guide.
| Parameters: | p : int Size of the test sets. |
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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 |
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| 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. |
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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. |
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| Yields: | train : ndarray The training set indices for that split. test : ndarray The testing set indices for that split. |