class sklearn.model_selection.PredefinedSplit(test_fold)
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Predefined split cross-validator
Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold
parameter.
Read more in the User Guide.
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>>> from sklearn.model_selection import PredefinedSplit >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> test_fold = [0, 1, -1, 1] >>> ps = PredefinedSplit(test_fold) >>> ps.get_n_splits() 2 >>> print(ps) PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) >>> for train_index, test_index in ps.split(): ... 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] TRAIN: [1 2 3] TEST: [0] TRAIN: [0 2] TEST: [1 3]
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__(test_fold)
[source]
get_n_splits(X=None, y=None, groups=None)
[source]
Returns the number of splitting iterations in the cross-validator
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Returns: |
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split(X=None, y=None, groups=None)
[source]
Generate indices to split data into training and test set.
Parameters: |
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Yields: |
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.PredefinedSplit.html