model_selection.LeaveOneOut

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) 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 KFold, ShuffleSplit or StratifiedKFold.

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
`split`(X[, y, groups])	Generate indices to split data into training and test set.

__init__() [source]

get_n_splits(X, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator

Parameters:

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.

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.

split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.

Parameters:

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.

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.

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html

doc_scikit_learn

2025-01-10 15:47:30

Comments