Warning
DEPRECATED
-
class sklearn.cross_validation.PredefinedSplit(test_fold)
[source] -
Predefined split cross validation iterator
Deprecated since version 0.18: This module will be removed in 0.20. Use
sklearn.model_selection.PredefinedSplit
instead.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_fold
parameter.Read more in the User Guide.
Parameters: test_fold : ?array-like, shape (n_samples,)
test_fold[i] gives the test set fold of sample i. A value of -1 indicates that the corresponding sample is not part of any test set folds, but will instead always be put into the training fold.
Examples
>>> from sklearn.cross_validation import PredefinedSplit >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([0, 0, 1, 1]) >>> ps = PredefinedSplit(test_fold=[0, 1, -1, 1]) >>> len(ps) 2 >>> print(ps) sklearn.cross_validation.PredefinedSplit(test_fold=[ 0 1 -1 1]) >>> for train_index, test_index in ps: ... 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] .. automethod:: __init__
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