set_class_box_param — Set system parameters for classification.
set_class_box_param modifies parameter which manipulate the training sequence while calling learn_class_box. Only parameters of the classifier are modified, all other classifiers remain unmodified. 'min_samples_for_split' is the number of examples at least which have to train in one cuboid of this classifier, before the cuboid is allowed to divide itself. 'split_error' indicates the critical error. By its exceeding the cuboid divides itself, if there are more than 'min_samples_for_split' examples to train. 'prop_constant' manipulates the extension of the cuboids. It is proportional to the average distance of the training examples in this cuboid to the center of the cuboid. More detailed:
extension x prop = average distance of the expectation value.
This relation is valid in every dimension. Hence inside a cuboid the dimensions of the feature space are supposed to be independent.
The parameters are set with problem independent default values, which must not modified without any reason. Parameters are only important during a learning sequence. They do not influence on the behavior of enquire_class_box.
'min_samples_for_split' = 80,
'split_error' = 0.1,
'prop_constant' = 0.25
Handle of the classifier.
Name of the wanted parameter.
Default value: 'split_error'
Suggested values: 'min_samples_for_split', 'split_error', 'prop_constant'
Value of the parameter.
Default value: 0.1
read_sampset returns 2 (H_MSG_TRUE).
learn_class_box, test_sampset_box, write_class_box, close_class_box, clear_sampset
enquire_class_box, get_class_box_param, learn_class_box