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class sklearn.model_selection.PredefinedSplit(test_fold)[source] -
Predefined split cross-validator
Splits the data into training/test set folds according to a predefined scheme. Each sample can be assigned to at most one test set fold, as specified by the user through the
test_foldparameter.Read more in the User Guide.
Examples
123456789101112131415>>>fromsklearn.model_selectionimportPredefinedSplit>>> 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]))>>>fortrain_index, test_indexinps.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: [123] TEST: [0]TRAIN: [02] TEST: [13]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__(test_fold)[source]
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get_n_splits(X=None, y=None, groups=None)[source] -
Returns the number of splitting iterations in the cross-validator
Parameters: X : object
Always ignored, exists for compatibility.
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=None, y=None, groups=None)[source] -
Generate indices to split data into training and test set.
Parameters: X : object
Always ignored, exists for compatibility.
y : object
Always ignored, exists for compatibility.
groups : object
Always ignored, exists for compatibility.
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.PredefinedSplit()
2025-01-10 15:47:30
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