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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
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LeaveOneGroupOut
- For splitting the data according to explicit, domain-specific stratification of the dataset.
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GroupKFold
- 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 split
(X[, y, groups])Generate indices to split data into training and test set. -
__init__()
[source]
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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.
Returns: n_splits : int
Returns the number of splitting iterations in the cross-validator.
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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.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
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model_selection.LeaveOneOut
2017-01-15 04:24:12
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