sklearn.model_selection.LeaveOneOut — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.model_selection. LeaveOneOut[source]¶
Leave-One-Out cross-validator
Provides train/test indices to split data in train/test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set.
Note: LeaveOneOut() is equivalent to KFold(n_splits=n) andLeavePOut(p=1) where n is the number of samples.
Due to the high number of test sets (which is the same as the number of samples) this cross-validation method can be very costly. For large datasets one should favor KFold, ShuffleSplitor StratifiedKFold.
Read more in the User Guide.
See also
For splitting the data according to explicit, domain-specific stratification of the dataset.
K-fold iterator variant with non-overlapping groups.
Examples
from sklearn.model_selection import LeaveOneOut X = np.array([[1, 2], [3, 4]]) y = np.array([1, 2]) loo = LeaveOneOut() loo.get_n_splits(X) 2 print(loo) LeaveOneOut() for train_index, test_index in loo.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] ... print(X_train, X_test, y_train, y_test) TRAIN: [1] TEST: [0] [[3 4]] [[1 2]] [2] [1] TRAIN: [0] TEST: [1] [[1 2]] [[3 4]] [1] [2]
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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| Returns: | n_splits : int Returns the number of splitting iterations in the cross-validator. |
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. |