 sklearn.model_selection.LeaveOneOut   classsklearn.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. 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 and n_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 and n_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.     
 
 

    
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