wildboar.model_selection._cv#

Module Contents#

Classes#

RepeatedOutlierSplit

Repeated random outlier cross-validator
class wildboar.model_selection._cv.RepeatedOutlierSplit(n_splits=None, *, test_size=0.2, n_outlier=0.05, shuffle=True, random_state=None)#

Repeated random outlier cross-validator

Yields indicies that split the dataset into training and test sets.

Note

Contrary to other cross-validation strategies, the random outlier cross-validator does not ensure that all folds will be different. Instead, the inlier samples are shuffled and new outlier samples are inserted in the training and test sets repeatedly.

get_n_splits(X, y, groups=None)#

Returns the number of splitting iterations in the cross-validator :param X: The samples :type X: object :param y: The labels :type y: object :param groups: Always ignored, exists for compatibility. :type groups: object

Returns:

n_splits – Returns the number of splitting iterations in the cross-validator.

Return type:

int

split(x, y, groups=None)#

Return training and test indicies

Parameters:

  • x (object) – Always ignored, exists for compatibility.

  • y (object) – The labels

  • groups (object, optional) – Always ignored, exists for compatibility.

Yields:

train_idx, test_idx (ndarray) – The training and test indicies

__repr__()#

Return repr(self).