class sklearn.model_selection.GroupShuffleSplit(n_splits=5, test_size=’default’, train_size=None, random_state=None)
[source]
Shuffle-Group(s)-Out cross-validation iterator
Provides randomized train/test indices to split data according to a third-party provided group. This group information can be used to encode arbitrary domain specific stratifications of the samples as integers.
For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits.
The difference between LeavePGroupsOut and GroupShuffleSplit is that the former generates splits using all subsets of size p
unique groups, whereas GroupShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique groups.
For example, a less computationally intensive alternative to LeavePGroupsOut(p=10)
would be GroupShuffleSplit(test_size=10, n_splits=100)
.
Note: The parameters test_size
and train_size
refer to groups, and not to samples, as in ShuffleSplit.
Parameters: |
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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__(n_splits=5, test_size=’default’, train_size=None, random_state=None)
[source]
get_n_splits(X=None, y=None, groups=None)
[source]
Returns the number of splitting iterations in the cross-validator
Parameters: |
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Returns: |
|
split(X, y=None, groups=None)
[source]
Generate indices to split data into training and test set.
Parameters: |
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Yields: |
|
Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state
to an integer.
sklearn.model_selection.GroupShuffleSplit
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html