class museotoolbox.cross_validation.SpatialLeaveAsideOut(distance_matrix, valid_size=0.5, n_repeats=False, random_state=False, verbose=False)[source]

Generate a Cross-Validation using the farthest distance between the training and validation samples.

  • distance_matrix (numpy.ndarray, shape [n_samples, n_samples]) – Array got from function samplingMethods.getdistance_matrixForDistanceCV(inRaster,inVector)

  • valid_size (float, default 0.5.) – The percentage of validaton to keep : from 0 to 1.

  • n_repeats (int or bool, optional (default=False)) – If False, n_repeats is 1/valid_size (default : 1/0.5 = 2) If int : will iterate the number of times given in n_repeats.

  • random_state (integer or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If None, the random number generator is created with time.time().

  • verbose (integer or False, optional (default=False)) – Controls the verbosity: the higher the value is, the more the messages are detailed.


See “Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution” :

Manage cross-validation methods to generate the duo valid/train samples.


__init__(distance_matrix[, valid_size, …])

Manage cross-validation methods to generate the duo valid/train samples.

get_n_splits([X, y, groups])

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



save_to_vector(vector, field[, group, …])

Save to vector files each fold from the cross-validation.

split(X, y[, groups])

Split the vector/array according to y and groups.

Examples using museotoolbox.cross_validation.SpatialLeaveAsideOut