sklearn.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 toKFold(n_splits=n)
andLeavePOut(p=1)
wheren
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
orStratifiedKFold
.Read more in the User Guide.
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.
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 = 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.
- get_n_splits(X, y=None, groups=None)[source]¶
Returns the number of splitting iterations in the cross-validator
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training data, where
n_samples
is the number of samples andn_features
is the number of features.- yobject
Always ignored, exists for compatibility.
- groupsobject
Always ignored, exists for compatibility.
- Returns
- n_splitsint
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
- Xarray-like of shape (n_samples, n_features)
Training data, where
n_samples
is the number of samples andn_features
is the number of features.- yarray-like of shape (n_samples,)
The target variable for supervised learning problems.
- groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set.
- Yields
- trainndarray
The training set indices for that split.
- testndarray
The testing set indices for that split.