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)`` and ``LeavePOut(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 :class:`KFold`, :class:`ShuffleSplit` or :class:`StratifiedKFold`.

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

Examples -------- >>> import numpy as np >>> 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 = Xtrain_index, Xtest_index ... y_train, y_test = ytrain_index, ytest_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

See also -------- LeaveOneGroupOut For splitting the data according to explicit, domain-specific stratification of the dataset.

GroupKFold: K-fold iterator variant with non-overlapping groups.

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.

Returns ------- n_splits : int Returns the number of splitting iterations in the cross-validator.

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.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.